Geo-Information Approaches for Water Quality Monitoring in Arid River Systems Using Machine Learning Article Swipe
Víctor Flores
,
Adel Elmaghraby
,
Rafael Martínez-Peláez
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0017.v1
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202501.0017.v1
Water is the most important resource for life; however, the intensifying exploitation of water resources has led to significant degradation in water quality, particularly in rivers. This study investigates the potential of predictive models based on artificial intelligence techniques, such as Support Vector Machine (SVM) combined with mathematical approaches such as the Water Quality Index (WQI), to enhance the forecasting of water quality. We detail the methodology employed to construct these predictive models utilizing SVM, a specific WQI in conjunction with domain expertise. The models are developed from historical physicochemical parameter datasets from seven monitoring stations along a section of the Loa River in Antofagasta, Chile. The performance of the SVM model was rigorously validated using four key metrics: accuracy (acc), precision (p), recall (r), and F1-score. This paper elucidates the processes of dataset curation, and threshold optimization for influential physicochemical parameters. The approach presented herein is innovative, as it marks the first attempt to predict water quality specifically for the Loa River using SVM. The resultant models demonstrate robust performance metrics, achieving mean values of acc = 0.866, p = 0.849, r = 0.863, and F1-score = 0.847, positioning them competitively against analogous studies employing alternative methodologies in similar contexts.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202501.0017.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406112469
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4406112469Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202501.0017.v1Digital Object Identifier
- Title
-
Geo-Information Approaches for Water Quality Monitoring in Arid River Systems Using Machine LearningWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-03Full publication date if available
- Authors
-
Víctor Flores, Adel Elmaghraby, Rafael Martínez-PeláezList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202501.0017.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202501.0017.v1Direct OA link when available
- Concepts
-
Support vector machine, Computer science, Water quality, Machine learning, Artificial intelligence, Water resources, Data mining, Quality (philosophy), Index (typography), Resource (disambiguation), Ecology, World Wide Web, Computer network, Philosophy, Biology, EpistemologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4406112469 |
|---|---|
| doi | https://doi.org/10.20944/preprints202501.0017.v1 |
| ids.doi | https://doi.org/10.20944/preprints202501.0017.v1 |
| ids.openalex | https://openalex.org/W4406112469 |
| fwci | |
| type | preprint |
| title | Geo-Information Approaches for Water Quality Monitoring in Arid River Systems Using Machine Learning |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11490 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9915000200271606 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2305 |
| topics[0].subfield.display_name | Environmental Engineering |
| topics[0].display_name | Hydrological Forecasting Using AI |
| topics[1].id | https://openalex.org/T11634 |
| topics[1].field.id | https://openalex.org/fields/23 |
| topics[1].field.display_name | Environmental Science |
| topics[1].score | 0.9753000140190125 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2312 |
| topics[1].subfield.display_name | Water Science and Technology |
| topics[1].display_name | Water Quality and Pollution Assessment |
| topics[2].id | https://openalex.org/T12697 |
| topics[2].field.id | https://openalex.org/fields/23 |
| topics[2].field.display_name | Environmental Science |
| topics[2].score | 0.9667999744415283 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2312 |
| topics[2].subfield.display_name | Water Science and Technology |
| topics[2].display_name | Water Quality Monitoring Technologies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C12267149 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7974181771278381 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q282453 |
| concepts[0].display_name | Support vector machine |
| concepts[1].id | https://openalex.org/C41008148 |
| concepts[1].level | 0 |
| concepts[1].score | 0.6110919117927551 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[1].display_name | Computer science |
| concepts[2].id | https://openalex.org/C2780797713 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5926410555839539 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q625376 |
| concepts[2].display_name | Water quality |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.565886378288269 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.5425252914428711 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C153823671 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5232763886451721 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1049799 |
| concepts[5].display_name | Water resources |
| concepts[6].id | https://openalex.org/C124101348 |
| concepts[6].level | 1 |
| concepts[6].score | 0.5029379725456238 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[6].display_name | Data mining |
| concepts[7].id | https://openalex.org/C2779530757 |
| concepts[7].level | 2 |
| concepts[7].score | 0.44974285364151 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q1207505 |
| concepts[7].display_name | Quality (philosophy) |
| concepts[8].id | https://openalex.org/C2777382242 |
| concepts[8].level | 2 |
| concepts[8].score | 0.4403517544269562 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q6017816 |
| concepts[8].display_name | Index (typography) |
| concepts[9].id | https://openalex.org/C206345919 |
| concepts[9].level | 2 |
| concepts[9].score | 0.43176597356796265 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q20380951 |
| concepts[9].display_name | Resource (disambiguation) |
| concepts[10].id | https://openalex.org/C18903297 |
| concepts[10].level | 1 |
| concepts[10].score | 0.08488881587982178 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7150 |
| concepts[10].display_name | Ecology |
| concepts[11].id | https://openalex.org/C136764020 |
| concepts[11].level | 1 |
| concepts[11].score | 0.0 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q466 |
| concepts[11].display_name | World Wide Web |
| concepts[12].id | https://openalex.org/C31258907 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1301371 |
| concepts[12].display_name | Computer network |
| concepts[13].id | https://openalex.org/C138885662 |
| concepts[13].level | 0 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q5891 |
| concepts[13].display_name | Philosophy |
| concepts[14].id | https://openalex.org/C86803240 |
| concepts[14].level | 0 |
| concepts[14].score | 0.0 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q420 |
| concepts[14].display_name | Biology |
| concepts[15].id | https://openalex.org/C111472728 |
| concepts[15].level | 1 |
| concepts[15].score | 0.0 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q9471 |
| concepts[15].display_name | Epistemology |
| keywords[0].id | https://openalex.org/keywords/support-vector-machine |
| keywords[0].score | 0.7974181771278381 |
| keywords[0].display_name | Support vector machine |
| keywords[1].id | https://openalex.org/keywords/computer-science |
| keywords[1].score | 0.6110919117927551 |
| keywords[1].display_name | Computer science |
| keywords[2].id | https://openalex.org/keywords/water-quality |
| keywords[2].score | 0.5926410555839539 |
| keywords[2].display_name | Water quality |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.565886378288269 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.5425252914428711 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/water-resources |
| keywords[5].score | 0.5232763886451721 |
| keywords[5].display_name | Water resources |
| keywords[6].id | https://openalex.org/keywords/data-mining |
| keywords[6].score | 0.5029379725456238 |
| keywords[6].display_name | Data mining |
| keywords[7].id | https://openalex.org/keywords/quality |
| keywords[7].score | 0.44974285364151 |
| keywords[7].display_name | Quality (philosophy) |
| keywords[8].id | https://openalex.org/keywords/index |
| keywords[8].score | 0.4403517544269562 |
| keywords[8].display_name | Index (typography) |
| keywords[9].id | https://openalex.org/keywords/resource |
| keywords[9].score | 0.43176597356796265 |
| keywords[9].display_name | Resource (disambiguation) |
| keywords[10].id | https://openalex.org/keywords/ecology |
| keywords[10].score | 0.08488881587982178 |
| keywords[10].display_name | Ecology |
| language | en |
| locations[0].id | doi:10.20944/preprints202501.0017.v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S6309402219 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Preprints.org |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| 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/10.20944/preprints202501.0017.v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5007615582 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-6995-9434 |
| authorships[0].author.display_name | Víctor Flores |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Víctor Flores |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5045337673 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-5274-8596 |
| authorships[1].author.display_name | Adel Elmaghraby |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Adel Elmagrhaby |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5076836614 |
| authorships[2].author.orcid | https://orcid.org/0000-0003-2188-9892 |
| authorships[2].author.display_name | Rafael Martínez-Peláez |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | Rafael Martinez-Pelaez |
| authorships[2].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.20944/preprints202501.0017.v1 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Geo-Information Approaches for Water Quality Monitoring in Arid River Systems Using Machine Learning |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11490 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9915000200271606 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2305 |
| primary_topic.subfield.display_name | Environmental Engineering |
| primary_topic.display_name | Hydrological Forecasting Using AI |
| related_works | https://openalex.org/W2090763504, https://openalex.org/W148178222, https://openalex.org/W2104657898, https://openalex.org/W1948992892, https://openalex.org/W1886884218, https://openalex.org/W1910826599, https://openalex.org/W2012353789, https://openalex.org/W2530420969, https://openalex.org/W2051187167, https://openalex.org/W1980100242 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.20944/preprints202501.0017.v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S6309402219 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| 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 | Preprints.org |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| 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/10.20944/preprints202501.0017.v1 |
| primary_location.id | doi:10.20944/preprints202501.0017.v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S6309402219 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Preprints.org |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| 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/10.20944/preprints202501.0017.v1 |
| publication_date | 2025-01-03 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.= | 177, 180, 183, 187 |
| abstract_inverted_index.a | 75, 97 |
| abstract_inverted_index.p | 179 |
| abstract_inverted_index.r | 182 |
| abstract_inverted_index.We | 63 |
| abstract_inverted_index.as | 40, 50, 148 |
| abstract_inverted_index.in | 20, 24, 78, 103, 198 |
| abstract_inverted_index.is | 1, 146 |
| abstract_inverted_index.it | 149 |
| abstract_inverted_index.of | 12, 31, 60, 99, 108, 132, 175 |
| abstract_inverted_index.on | 35 |
| abstract_inverted_index.to | 17, 56, 68, 154 |
| abstract_inverted_index.Loa | 101, 161 |
| abstract_inverted_index.SVM | 110 |
| abstract_inverted_index.The | 83, 106, 142, 165 |
| abstract_inverted_index.WQI | 77 |
| abstract_inverted_index.acc | 176 |
| abstract_inverted_index.and | 125, 135, 185 |
| abstract_inverted_index.are | 85 |
| abstract_inverted_index.for | 6, 138, 159 |
| abstract_inverted_index.has | 15 |
| abstract_inverted_index.key | 117 |
| abstract_inverted_index.led | 16 |
| abstract_inverted_index.the | 2, 9, 29, 51, 58, 65, 100, 109, 130, 151, 160 |
| abstract_inverted_index.was | 112 |
| abstract_inverted_index.(p), | 122 |
| abstract_inverted_index.(r), | 124 |
| abstract_inverted_index.SVM, | 74 |
| abstract_inverted_index.SVM. | 164 |
| abstract_inverted_index.This | 26, 127 |
| abstract_inverted_index.four | 116 |
| abstract_inverted_index.from | 87, 92 |
| abstract_inverted_index.mean | 173 |
| abstract_inverted_index.most | 3 |
| abstract_inverted_index.such | 39, 49 |
| abstract_inverted_index.them | 190 |
| abstract_inverted_index.with | 46, 80 |
| abstract_inverted_index.(SVM) | 44 |
| abstract_inverted_index.Index | 54 |
| abstract_inverted_index.River | 102, 162 |
| abstract_inverted_index.Water | 0, 52 |
| abstract_inverted_index.along | 96 |
| abstract_inverted_index.based | 34 |
| abstract_inverted_index.first | 152 |
| abstract_inverted_index.life; | 7 |
| abstract_inverted_index.marks | 150 |
| abstract_inverted_index.model | 111 |
| abstract_inverted_index.paper | 128 |
| abstract_inverted_index.seven | 93 |
| abstract_inverted_index.study | 27 |
| abstract_inverted_index.these | 70 |
| abstract_inverted_index.using | 115, 163 |
| abstract_inverted_index.water | 13, 21, 61, 156 |
| abstract_inverted_index.(WQI), | 55 |
| abstract_inverted_index.(acc), | 120 |
| abstract_inverted_index.0.847, | 188 |
| abstract_inverted_index.0.849, | 181 |
| abstract_inverted_index.0.863, | 184 |
| abstract_inverted_index.0.866, | 178 |
| abstract_inverted_index.Chile. | 105 |
| abstract_inverted_index.Vector | 42 |
| abstract_inverted_index.detail | 64 |
| abstract_inverted_index.domain | 81 |
| abstract_inverted_index.herein | 145 |
| abstract_inverted_index.models | 33, 72, 84, 167 |
| abstract_inverted_index.recall | 123 |
| abstract_inverted_index.robust | 169 |
| abstract_inverted_index.values | 174 |
| abstract_inverted_index.Machine | 43 |
| abstract_inverted_index.Quality | 53 |
| abstract_inverted_index.Support | 41 |
| abstract_inverted_index.against | 192 |
| abstract_inverted_index.attempt | 153 |
| abstract_inverted_index.dataset | 133 |
| abstract_inverted_index.enhance | 57 |
| abstract_inverted_index.predict | 155 |
| abstract_inverted_index.quality | 157 |
| abstract_inverted_index.rivers. | 25 |
| abstract_inverted_index.section | 98 |
| abstract_inverted_index.similar | 199 |
| abstract_inverted_index.studies | 194 |
| abstract_inverted_index.F1-score | 186 |
| abstract_inverted_index.accuracy | 119 |
| abstract_inverted_index.approach | 143 |
| abstract_inverted_index.combined | 45 |
| abstract_inverted_index.datasets | 91 |
| abstract_inverted_index.employed | 67 |
| abstract_inverted_index.however, | 8 |
| abstract_inverted_index.metrics, | 171 |
| abstract_inverted_index.metrics: | 118 |
| abstract_inverted_index.quality, | 22 |
| abstract_inverted_index.quality. | 62 |
| abstract_inverted_index.resource | 5 |
| abstract_inverted_index.specific | 76 |
| abstract_inverted_index.stations | 95 |
| abstract_inverted_index.F1-score. | 126 |
| abstract_inverted_index.achieving | 172 |
| abstract_inverted_index.analogous | 193 |
| abstract_inverted_index.construct | 69 |
| abstract_inverted_index.contexts. | 200 |
| abstract_inverted_index.curation, | 134 |
| abstract_inverted_index.developed | 86 |
| abstract_inverted_index.employing | 195 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.parameter | 90 |
| abstract_inverted_index.potential | 30 |
| abstract_inverted_index.precision | 121 |
| abstract_inverted_index.presented | 144 |
| abstract_inverted_index.processes | 131 |
| abstract_inverted_index.resources | 14 |
| abstract_inverted_index.resultant | 166 |
| abstract_inverted_index.threshold | 136 |
| abstract_inverted_index.utilizing | 73 |
| abstract_inverted_index.validated | 114 |
| abstract_inverted_index.approaches | 48 |
| abstract_inverted_index.artificial | 36 |
| abstract_inverted_index.elucidates | 129 |
| abstract_inverted_index.expertise. | 82 |
| abstract_inverted_index.historical | 88 |
| abstract_inverted_index.monitoring | 94 |
| abstract_inverted_index.predictive | 32, 71 |
| abstract_inverted_index.rigorously | 113 |
| abstract_inverted_index.alternative | 196 |
| abstract_inverted_index.conjunction | 79 |
| abstract_inverted_index.degradation | 19 |
| abstract_inverted_index.demonstrate | 168 |
| abstract_inverted_index.forecasting | 59 |
| abstract_inverted_index.influential | 139 |
| abstract_inverted_index.innovative, | 147 |
| abstract_inverted_index.methodology | 66 |
| abstract_inverted_index.parameters. | 141 |
| abstract_inverted_index.performance | 107, 170 |
| abstract_inverted_index.positioning | 189 |
| abstract_inverted_index.significant | 18 |
| abstract_inverted_index.techniques, | 38 |
| abstract_inverted_index.Antofagasta, | 104 |
| abstract_inverted_index.exploitation | 11 |
| abstract_inverted_index.intelligence | 37 |
| abstract_inverted_index.intensifying | 10 |
| abstract_inverted_index.investigates | 28 |
| abstract_inverted_index.mathematical | 47 |
| abstract_inverted_index.optimization | 137 |
| abstract_inverted_index.particularly | 23 |
| abstract_inverted_index.specifically | 158 |
| abstract_inverted_index.competitively | 191 |
| abstract_inverted_index.methodologies | 197 |
| abstract_inverted_index.physicochemical | 89, 140 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/6 |
| sustainable_development_goals[0].score | 0.8399999737739563 |
| sustainable_development_goals[0].display_name | Clean water and sanitation |
| citation_normalized_percentile.value | 0.00601013 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |