A three-dimensional functional data geometric morphometrics approach for exploring shape variation Article Swipe
Aneesha Balachandran Pillay
,
Sophie Dabo‐Niang
,
Dharini Pathmanathan
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3254348/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-3254348/v1
This research introduces a new method for analysing shape variation for 3D landmark coordinate data, called functional data geometric morphometrics (FDGM). FDGM uses functional data analysis (FDA) to treat landmark coordinates as continuous curves or functions. This allows for a more exhaustive description and analysis of shape variation compared to geometric morphometrics (GM), which treats landmark coordinates as discrete points. A simulation study was conducted to demonstrate the general effectiveness of FDGM compared to the GM. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to both the landmark coordinates and the functional form of the landmark coordinates. The analyses favoured FDGM. The reconstruction error for FDGM was smaller when smoothed data was considered in generating the data. FDGM and GM were then applied to distinguish dietary categories of kangaroos (omnivores, mixed feeders, browser, and grazer) using landmarks obtained from crania of 41 kangaroo extant species. The results demonstrate that FDGM is a powerful method for analysing shape variation in 3D landmark coordinate data. FDGM can substantially enhance the domain of morphometrics, providing a valuable resource for driving future progress within this realm.
Related Topics
Concepts
Landmark
Morphometrics
Principal component analysis
Shape analysis (program analysis)
Artificial intelligence
Functional data analysis
Crania
Linear discriminant analysis
Pattern recognition (psychology)
Variation (astronomy)
Barycentric coordinate system
Computer science
Mathematics
Geography
Biology
Geometry
Machine learning
Ecology
Archaeology
Physics
Programming language
Static analysis
Astrophysics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-3254348/v1
- https://www.researchsquare.com/article/rs-3254348/latest.pdf
- OA Status
- green
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385953613
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4385953613Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-3254348/v1Digital Object Identifier
- Title
-
A three-dimensional functional data geometric morphometrics approach for exploring shape variationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-17Full publication date if available
- Authors
-
Aneesha Balachandran Pillay, Sophie Dabo‐Niang, Dharini PathmanathanList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-3254348/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-3254348/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-3254348/latest.pdfDirect OA link when available
- Concepts
-
Landmark, Morphometrics, Principal component analysis, Shape analysis (program analysis), Artificial intelligence, Functional data analysis, Crania, Linear discriminant analysis, Pattern recognition (psychology), Variation (astronomy), Barycentric coordinate system, Computer science, Mathematics, Geography, Biology, Geometry, Machine learning, Ecology, Archaeology, Physics, Programming language, Static analysis, AstrophysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
32Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4385953613 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-3254348/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-3254348/v1 |
| ids.openalex | https://openalex.org/W4385953613 |
| fwci | 0.0 |
| type | preprint |
| title | A three-dimensional functional data geometric morphometrics approach for exploring shape variation |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12417 |
| topics[0].field.id | https://openalex.org/fields/26 |
| topics[0].field.display_name | Mathematics |
| topics[0].score | 0.9990000128746033 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2608 |
| topics[0].subfield.display_name | Geometry and Topology |
| topics[0].display_name | Morphological variations and asymmetry |
| topics[1].id | https://openalex.org/T10594 |
| topics[1].field.id | https://openalex.org/fields/13 |
| topics[1].field.display_name | Biochemistry, Genetics and Molecular Biology |
| topics[1].score | 0.9961000084877014 |
| topics[1].domain.id | https://openalex.org/domains/1 |
| topics[1].domain.display_name | Life Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1311 |
| topics[1].subfield.display_name | Genetics |
| topics[1].display_name | Genetic and phenotypic traits in livestock |
| topics[2].id | https://openalex.org/T10199 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.991599977016449 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2303 |
| topics[2].subfield.display_name | Ecology |
| topics[2].display_name | Wildlife Ecology and Conservation |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C2780297707 |
| concepts[0].level | 2 |
| concepts[0].score | 0.9440044164657593 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q4895393 |
| concepts[0].display_name | Landmark |
| concepts[1].id | https://openalex.org/C33511622 |
| concepts[1].level | 2 |
| concepts[1].score | 0.9053013324737549 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q1851434 |
| concepts[1].display_name | Morphometrics |
| concepts[2].id | https://openalex.org/C27438332 |
| concepts[2].level | 2 |
| concepts[2].score | 0.7278415560722351 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q2873 |
| concepts[2].display_name | Principal component analysis |
| concepts[3].id | https://openalex.org/C112604564 |
| concepts[3].level | 3 |
| concepts[3].score | 0.6005004644393921 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q7489226 |
| concepts[3].display_name | Shape analysis (program analysis) |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5213543176651001 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C51820054 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4917962849140167 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q5508814 |
| concepts[5].display_name | Functional data analysis |
| concepts[6].id | https://openalex.org/C2775915008 |
| concepts[6].level | 2 |
| concepts[6].score | 0.47293758392333984 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q5182118 |
| concepts[6].display_name | Crania |
| concepts[7].id | https://openalex.org/C69738355 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4584175944328308 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1228929 |
| concepts[7].display_name | Linear discriminant analysis |
| concepts[8].id | https://openalex.org/C153180895 |
| concepts[8].level | 2 |
| concepts[8].score | 0.45529013872146606 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q7148389 |
| concepts[8].display_name | Pattern recognition (psychology) |
| concepts[9].id | https://openalex.org/C2778334786 |
| concepts[9].level | 2 |
| concepts[9].score | 0.4488877058029175 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q1586270 |
| concepts[9].display_name | Variation (astronomy) |
| concepts[10].id | https://openalex.org/C128356825 |
| concepts[10].level | 2 |
| concepts[10].score | 0.4355139434337616 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q738422 |
| concepts[10].display_name | Barycentric coordinate system |
| concepts[11].id | https://openalex.org/C41008148 |
| concepts[11].level | 0 |
| concepts[11].score | 0.43033134937286377 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[11].display_name | Computer science |
| concepts[12].id | https://openalex.org/C33923547 |
| concepts[12].level | 0 |
| concepts[12].score | 0.3305117189884186 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q395 |
| concepts[12].display_name | Mathematics |
| concepts[13].id | https://openalex.org/C205649164 |
| concepts[13].level | 0 |
| concepts[13].score | 0.311659038066864 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q1071 |
| concepts[13].display_name | Geography |
| concepts[14].id | https://openalex.org/C86803240 |
| concepts[14].level | 0 |
| concepts[14].score | 0.20516818761825562 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[14].display_name | Biology |
| concepts[15].id | https://openalex.org/C2524010 |
| concepts[15].level | 1 |
| concepts[15].score | 0.1997954547405243 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q8087 |
| concepts[15].display_name | Geometry |
| concepts[16].id | https://openalex.org/C119857082 |
| concepts[16].level | 1 |
| concepts[16].score | 0.1437891125679016 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[16].display_name | Machine learning |
| concepts[17].id | https://openalex.org/C18903297 |
| concepts[17].level | 1 |
| concepts[17].score | 0.14109212160110474 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[17].display_name | Ecology |
| concepts[18].id | https://openalex.org/C166957645 |
| concepts[18].level | 1 |
| concepts[18].score | 0.0 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q23498 |
| concepts[18].display_name | Archaeology |
| concepts[19].id | https://openalex.org/C121332964 |
| concepts[19].level | 0 |
| concepts[19].score | 0.0 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[19].display_name | Physics |
| concepts[20].id | https://openalex.org/C199360897 |
| concepts[20].level | 1 |
| concepts[20].score | 0.0 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q9143 |
| concepts[20].display_name | Programming language |
| concepts[21].id | https://openalex.org/C97686452 |
| concepts[21].level | 2 |
| concepts[21].score | 0.0 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q7604153 |
| concepts[21].display_name | Static analysis |
| concepts[22].id | https://openalex.org/C44870925 |
| concepts[22].level | 1 |
| concepts[22].score | 0.0 |
| concepts[22].wikidata | https://www.wikidata.org/wiki/Q37547 |
| concepts[22].display_name | Astrophysics |
| keywords[0].id | https://openalex.org/keywords/landmark |
| keywords[0].score | 0.9440044164657593 |
| keywords[0].display_name | Landmark |
| keywords[1].id | https://openalex.org/keywords/morphometrics |
| keywords[1].score | 0.9053013324737549 |
| keywords[1].display_name | Morphometrics |
| keywords[2].id | https://openalex.org/keywords/principal-component-analysis |
| keywords[2].score | 0.7278415560722351 |
| keywords[2].display_name | Principal component analysis |
| keywords[3].id | https://openalex.org/keywords/shape-analysis |
| keywords[3].score | 0.6005004644393921 |
| keywords[3].display_name | Shape analysis (program analysis) |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5213543176651001 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/functional-data-analysis |
| keywords[5].score | 0.4917962849140167 |
| keywords[5].display_name | Functional data analysis |
| keywords[6].id | https://openalex.org/keywords/crania |
| keywords[6].score | 0.47293758392333984 |
| keywords[6].display_name | Crania |
| keywords[7].id | https://openalex.org/keywords/linear-discriminant-analysis |
| keywords[7].score | 0.4584175944328308 |
| keywords[7].display_name | Linear discriminant analysis |
| keywords[8].id | https://openalex.org/keywords/pattern-recognition |
| keywords[8].score | 0.45529013872146606 |
| keywords[8].display_name | Pattern recognition (psychology) |
| keywords[9].id | https://openalex.org/keywords/variation |
| keywords[9].score | 0.4488877058029175 |
| keywords[9].display_name | Variation (astronomy) |
| keywords[10].id | https://openalex.org/keywords/barycentric-coordinate-system |
| keywords[10].score | 0.4355139434337616 |
| keywords[10].display_name | Barycentric coordinate system |
| keywords[11].id | https://openalex.org/keywords/computer-science |
| keywords[11].score | 0.43033134937286377 |
| keywords[11].display_name | Computer science |
| keywords[12].id | https://openalex.org/keywords/mathematics |
| keywords[12].score | 0.3305117189884186 |
| keywords[12].display_name | Mathematics |
| keywords[13].id | https://openalex.org/keywords/geography |
| keywords[13].score | 0.311659038066864 |
| keywords[13].display_name | Geography |
| keywords[14].id | https://openalex.org/keywords/biology |
| keywords[14].score | 0.20516818761825562 |
| keywords[14].display_name | Biology |
| keywords[15].id | https://openalex.org/keywords/geometry |
| keywords[15].score | 0.1997954547405243 |
| keywords[15].display_name | Geometry |
| keywords[16].id | https://openalex.org/keywords/machine-learning |
| keywords[16].score | 0.1437891125679016 |
| keywords[16].display_name | Machine learning |
| keywords[17].id | https://openalex.org/keywords/ecology |
| keywords[17].score | 0.14109212160110474 |
| keywords[17].display_name | Ecology |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-3254348/v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402450 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Research Square (Research Square) |
| locations[0].source.host_organization | https://openalex.org/I4210096694 |
| locations[0].source.host_organization_name | Research Square (United States) |
| locations[0].source.host_organization_lineage | https://openalex.org/I4210096694 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-3254348/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-3254348/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5041515947 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-5137-9968 |
| authorships[0].author.display_name | Aneesha Balachandran Pillay |
| authorships[0].countries | MY |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[0].affiliations[0].raw_affiliation_string | Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur |
| authorships[0].institutions[0].id | https://openalex.org/I33849332 |
| authorships[0].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[0].institutions[0].country_code | MY |
| authorships[0].institutions[0].display_name | University of Malaya |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Aneesha Balachandran Pillay |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur |
| authorships[1].author.id | https://openalex.org/A5049255136 |
| authorships[1].author.orcid | https://orcid.org/0000-0002-4000-6752 |
| authorships[1].author.display_name | Sophie Dabo‐Niang |
| authorships[1].countries | FR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I1326498283, https://openalex.org/I4210145948 |
| authorships[1].affiliations[0].raw_affiliation_string | Université Lille, CNRS, UMR 8524-Laboratoire Paul Painlevé, INRIA-MODAL |
| authorships[1].institutions[0].id | https://openalex.org/I1326498283 |
| authorships[1].institutions[0].ror | https://ror.org/02kvxyf05 |
| authorships[1].institutions[0].type | government |
| authorships[1].institutions[0].lineage | https://openalex.org/I1326498283 |
| authorships[1].institutions[0].country_code | FR |
| authorships[1].institutions[0].display_name | Institut national de recherche en sciences et technologies du numérique |
| authorships[1].institutions[1].id | https://openalex.org/I4210145948 |
| authorships[1].institutions[1].ror | https://ror.org/043n4fm24 |
| authorships[1].institutions[1].type | facility |
| authorships[1].institutions[1].lineage | https://openalex.org/I1294671590, https://openalex.org/I1294671590, https://openalex.org/I1326498283, https://openalex.org/I2279609970, https://openalex.org/I4210141950, https://openalex.org/I4210145948 |
| authorships[1].institutions[1].country_code | FR |
| authorships[1].institutions[1].display_name | Laboratoire Paul Painlevé |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Sophie Dabo-Niang |
| authorships[1].is_corresponding | True |
| authorships[1].raw_affiliation_strings | Université Lille, CNRS, UMR 8524-Laboratoire Paul Painlevé, INRIA-MODAL |
| authorships[2].author.id | https://openalex.org/A5016896235 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-3279-4031 |
| authorships[2].author.display_name | Dharini Pathmanathan |
| authorships[2].countries | MY |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I33849332 |
| authorships[2].affiliations[0].raw_affiliation_string | Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur |
| authorships[2].institutions[0].id | https://openalex.org/I33849332 |
| authorships[2].institutions[0].ror | https://ror.org/00rzspn62 |
| authorships[2].institutions[0].type | education |
| authorships[2].institutions[0].lineage | https://openalex.org/I33849332 |
| authorships[2].institutions[0].country_code | MY |
| authorships[2].institutions[0].display_name | University of Malaya |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Dharini Pathmanathan |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Institute of Mathematical Sciences, Faculty of Science, Universiti Malaya, 50603 Kuala Lumpur |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-3254348/latest.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A three-dimensional functional data geometric morphometrics approach for exploring shape variation |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12417 |
| primary_topic.field.id | https://openalex.org/fields/26 |
| primary_topic.field.display_name | Mathematics |
| primary_topic.score | 0.9990000128746033 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2608 |
| primary_topic.subfield.display_name | Geometry and Topology |
| primary_topic.display_name | Morphological variations and asymmetry |
| related_works | https://openalex.org/W4242911702, https://openalex.org/W3204493048, https://openalex.org/W2955565995, https://openalex.org/W2560043495, https://openalex.org/W2761780533, https://openalex.org/W82940782, https://openalex.org/W2132661331, https://openalex.org/W2621624143, https://openalex.org/W3133925863, https://openalex.org/W2007422949 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-3254348/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402450 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Square (Research Square) |
| best_oa_location.source.host_organization | https://openalex.org/I4210096694 |
| best_oa_location.source.host_organization_name | Research Square (United States) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-3254348/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-3254348/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-3254348/v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402450 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Research Square (Research Square) |
| primary_location.source.host_organization | https://openalex.org/I4210096694 |
| primary_location.source.host_organization_name | Research Square (United States) |
| primary_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-3254348/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-3254348/v1 |
| publication_date | 2023-08-17 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2128971747, https://openalex.org/W2025734273, https://openalex.org/W3035511126, https://openalex.org/W3169800839, https://openalex.org/W2965529832, https://openalex.org/W2168407592, https://openalex.org/W2001619934, https://openalex.org/W2740754384, https://openalex.org/W3035942316, https://openalex.org/W147128559, https://openalex.org/W2230970864, https://openalex.org/W2724818314, https://openalex.org/W2039824374, https://openalex.org/W2761780533, https://openalex.org/W1984080040, https://openalex.org/W1978500508, https://openalex.org/W2553319916, https://openalex.org/W2147052720, https://openalex.org/W3021971632, https://openalex.org/W4240093485, https://openalex.org/W4292156489, https://openalex.org/W1502922572, https://openalex.org/W4236288200, https://openalex.org/W2037357509, https://openalex.org/W2529762274, https://openalex.org/W2042388438, https://openalex.org/W1513618424, https://openalex.org/W2805964580, https://openalex.org/W3045497881, https://openalex.org/W4384790478, https://openalex.org/W2144148350, https://openalex.org/W3017530280 |
| referenced_works_count | 32 |
| abstract_inverted_index.A | 61 |
| abstract_inverted_index.a | 4, 40, 155, 176 |
| abstract_inverted_index.3D | 12, 163 |
| abstract_inverted_index.41 | 145 |
| abstract_inverted_index.GM | 123 |
| abstract_inverted_index.as | 32, 58 |
| abstract_inverted_index.in | 117, 162 |
| abstract_inverted_index.is | 154 |
| abstract_inverted_index.of | 46, 71, 97, 131, 144, 173 |
| abstract_inverted_index.or | 35 |
| abstract_inverted_index.to | 28, 50, 66, 74, 88, 127 |
| abstract_inverted_index.GM. | 76 |
| abstract_inverted_index.The | 101, 105, 149 |
| abstract_inverted_index.and | 44, 81, 93, 122, 137 |
| abstract_inverted_index.can | 168 |
| abstract_inverted_index.for | 7, 11, 39, 108, 158, 179 |
| abstract_inverted_index.new | 5 |
| abstract_inverted_index.the | 68, 75, 90, 94, 98, 119, 171 |
| abstract_inverted_index.was | 64, 110, 115 |
| abstract_inverted_index.FDGM | 22, 72, 109, 121, 153, 167 |
| abstract_inverted_index.This | 1, 37 |
| abstract_inverted_index.both | 89 |
| abstract_inverted_index.data | 18, 25, 114 |
| abstract_inverted_index.form | 96 |
| abstract_inverted_index.from | 142 |
| abstract_inverted_index.more | 41 |
| abstract_inverted_index.that | 152 |
| abstract_inverted_index.then | 125 |
| abstract_inverted_index.this | 184 |
| abstract_inverted_index.uses | 23 |
| abstract_inverted_index.were | 86, 124 |
| abstract_inverted_index.when | 112 |
| abstract_inverted_index.(FDA) | 27 |
| abstract_inverted_index.(GM), | 53 |
| abstract_inverted_index.(LDA) | 85 |
| abstract_inverted_index.(PCA) | 80 |
| abstract_inverted_index.FDGM. | 104 |
| abstract_inverted_index.data, | 15 |
| abstract_inverted_index.data. | 120, 166 |
| abstract_inverted_index.error | 107 |
| abstract_inverted_index.mixed | 134 |
| abstract_inverted_index.shape | 9, 47, 160 |
| abstract_inverted_index.study | 63 |
| abstract_inverted_index.treat | 29 |
| abstract_inverted_index.using | 139 |
| abstract_inverted_index.which | 54 |
| abstract_inverted_index.allows | 38 |
| abstract_inverted_index.called | 16 |
| abstract_inverted_index.crania | 143 |
| abstract_inverted_index.curves | 34 |
| abstract_inverted_index.domain | 172 |
| abstract_inverted_index.extant | 147 |
| abstract_inverted_index.future | 181 |
| abstract_inverted_index.linear | 82 |
| abstract_inverted_index.method | 6, 157 |
| abstract_inverted_index.realm. | 185 |
| abstract_inverted_index.treats | 55 |
| abstract_inverted_index.within | 183 |
| abstract_inverted_index.(FDGM). | 21 |
| abstract_inverted_index.applied | 87, 126 |
| abstract_inverted_index.dietary | 129 |
| abstract_inverted_index.driving | 180 |
| abstract_inverted_index.enhance | 170 |
| abstract_inverted_index.general | 69 |
| abstract_inverted_index.grazer) | 138 |
| abstract_inverted_index.points. | 60 |
| abstract_inverted_index.results | 150 |
| abstract_inverted_index.smaller | 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analyses | 102 |
| abstract_inverted_index.analysis | 26, 45, 79, 84 |
| abstract_inverted_index.browser, | 136 |
| abstract_inverted_index.compared | 49, 73 |
| abstract_inverted_index.discrete | 59 |
| abstract_inverted_index.favoured | 103 |
| abstract_inverted_index.feeders, | 135 |
| abstract_inverted_index.kangaroo | 146 |
| abstract_inverted_index.landmark | 13, 30, 56, 91, 99, 164 |
| abstract_inverted_index.obtained | 141 |
| abstract_inverted_index.powerful | 156 |
| abstract_inverted_index.progress | 182 |
| abstract_inverted_index.research | 2 |
| abstract_inverted_index.resource | 178 |
| abstract_inverted_index.smoothed | 113 |
| abstract_inverted_index.species. | 148 |
| abstract_inverted_index.valuable | 177 |
| abstract_inverted_index.Principal | 77 |
| abstract_inverted_index.analysing | 8, 159 |
| abstract_inverted_index.component | 78 |
| abstract_inverted_index.conducted | 65 |
| abstract_inverted_index.geometric | 19, 51 |
| abstract_inverted_index.kangaroos | 132 |
| abstract_inverted_index.landmarks | 140 |
| abstract_inverted_index.providing | 175 |
| abstract_inverted_index.variation | 10, 48, 161 |
| abstract_inverted_index.categories | 130 |
| abstract_inverted_index.considered | 116 |
| abstract_inverted_index.continuous | 33 |
| abstract_inverted_index.coordinate | 14, 165 |
| abstract_inverted_index.exhaustive | 42 |
| abstract_inverted_index.functional | 17, 24, 95 |
| abstract_inverted_index.functions. | 36 |
| abstract_inverted_index.generating | 118 |
| abstract_inverted_index.introduces | 3 |
| abstract_inverted_index.simulation | 62 |
| abstract_inverted_index.(omnivores, | 133 |
| abstract_inverted_index.coordinates | 31, 57, 92 |
| abstract_inverted_index.demonstrate | 67, 151 |
| abstract_inverted_index.description | 43 |
| abstract_inverted_index.distinguish | 128 |
| abstract_inverted_index.coordinates. | 100 |
| abstract_inverted_index.discriminant | 83 |
| abstract_inverted_index.effectiveness | 70 |
| abstract_inverted_index.morphometrics | 20, 52 |
| abstract_inverted_index.substantially | 169 |
| abstract_inverted_index.morphometrics, | 174 |
| abstract_inverted_index.reconstruction | 106 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5049255136 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I1326498283, https://openalex.org/I4210145948 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/10 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Reduced inequalities |
| citation_normalized_percentile.value | 0.22491421 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |