Estimation of Biomass Water Equivalent via the Cosmic Ray Neutron Sensor Article Swipe
Trenton E. Franz
,
Ammar Wahbi
,
W. A. Avery
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-319-69539-6_4
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-319-69539-6_4
The CRNS functions at its most fundamental level as a detector of the hydrogen within its area of influence (circle of radius ~ 250 m). As such, hydrogen other than that within the water molecules in the soil is detected. A series of calibration equations have been developed to quantify and eliminate these sources of hydrogen so that the signal of soil moisture can be isolated [1–6]. McJannet et al. [7] demonstrated that soil moisture is the largest contributor of hydrogen to the signal of the CRNS with growing biomass contributing only slightly. These data show that in an agricultural environment, the most significant source of error comes in the form of soil lattice water (i.e., hydrogen molecules integrated into mineral structures and bound water between mineral grains not released at oven drying temperatures of 105 °C for 24 h) and from water vapor in the atmosphere. Despite this, growing biomass if left unquantified remains a source of uncertainty that must be addressed, partly in fast-growing agricultural crops. Much of the current and past research into the CRNS in agricultural environments focuses on its use as a sensor of soil moisture. However, there have also been investigations into its use as a tool for estimating growing crop biomass itself [8, 9]. Note that the biomass signal is fairly small and challenging to remove from the soil moisture signal and inherent noise in the neutron counts. This requires use of large detectors (i.e., high count rates on the order of 5000 to 10,000 to minimize uncertainty) and certain biomass detection limits (i.e., on the order of 0.5 kg/m2). Nevertheless the technique is theoretically sound [6] and an area of active research.
Related Topics
Concepts
Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-319-69539-6_4
- https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdf
- OA Status
- gold
- Cited By
- 10
- References
- 12
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2795446771
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2795446771Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1007/978-3-319-69539-6_4Digital Object Identifier
- Title
-
Estimation of Biomass Water Equivalent via the Cosmic Ray Neutron SensorWork title
- Type
-
book-chapterOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2018Year of publication
- Publication date
-
2018-01-01Full publication date if available
- Authors
-
Trenton E. Franz, Ammar Wahbi, W. A. AveryList of authors in order
- Landing page
-
https://doi.org/10.1007/978-3-319-69539-6_4Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdfDirect OA link when available
- Concepts
-
Biomass (ecology), Water content, Moisture, Environmental science, Soil science, Detector, Hydrogen, Agronomy, Meteorology, Physics, Optics, Geology, Geotechnical engineering, Quantum mechanics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
10Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2021: 9Per-year citation counts (last 5 years)
- References (count)
-
12Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W2795446771 |
|---|---|
| doi | https://doi.org/10.1007/978-3-319-69539-6_4 |
| ids.doi | https://doi.org/10.1007/978-3-319-69539-6_4 |
| ids.mag | 2795446771 |
| ids.openalex | https://openalex.org/W2795446771 |
| fwci | 4.01668624 |
| type | book-chapter |
| title | Estimation of Biomass Water Equivalent via the Cosmic Ray Neutron Sensor |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | 32 |
| biblio.first_page | 25 |
| topics[0].id | https://openalex.org/T11312 |
| topics[0].field.id | https://openalex.org/fields/23 |
| topics[0].field.display_name | Environmental Science |
| topics[0].score | 0.9972000122070312 |
| 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 | Soil Moisture and Remote Sensing |
| topics[1].id | https://openalex.org/T11333 |
| topics[1].field.id | https://openalex.org/fields/19 |
| topics[1].field.display_name | Earth and Planetary Sciences |
| topics[1].score | 0.9879999756813049 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1902 |
| topics[1].subfield.display_name | Atmospheric Science |
| topics[1].display_name | Climate change and permafrost |
| topics[2].id | https://openalex.org/T10572 |
| topics[2].field.id | https://openalex.org/fields/19 |
| topics[2].field.display_name | Earth and Planetary Sciences |
| topics[2].score | 0.984499990940094 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1908 |
| topics[2].subfield.display_name | Geophysics |
| topics[2].display_name | Geophysical and Geoelectrical Methods |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C115540264 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6591202020645142 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q2945560 |
| concepts[0].display_name | Biomass (ecology) |
| concepts[1].id | https://openalex.org/C24939127 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6428223252296448 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q373499 |
| concepts[1].display_name | Water content |
| concepts[2].id | https://openalex.org/C176864760 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5776764154434204 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q217651 |
| concepts[2].display_name | Moisture |
| concepts[3].id | https://openalex.org/C39432304 |
| concepts[3].level | 0 |
| concepts[3].score | 0.555756151676178 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[3].display_name | Environmental science |
| concepts[4].id | https://openalex.org/C159390177 |
| concepts[4].level | 1 |
| concepts[4].score | 0.49802327156066895 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q9161265 |
| concepts[4].display_name | Soil science |
| concepts[5].id | https://openalex.org/C94915269 |
| concepts[5].level | 2 |
| concepts[5].score | 0.4515109658241272 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1834857 |
| concepts[5].display_name | Detector |
| concepts[6].id | https://openalex.org/C512968161 |
| concepts[6].level | 2 |
| concepts[6].score | 0.4300428628921509 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q556 |
| concepts[6].display_name | Hydrogen |
| concepts[7].id | https://openalex.org/C6557445 |
| concepts[7].level | 1 |
| concepts[7].score | 0.23065125942230225 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q173113 |
| concepts[7].display_name | Agronomy |
| concepts[8].id | https://openalex.org/C153294291 |
| concepts[8].level | 1 |
| concepts[8].score | 0.22270655632019043 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q25261 |
| concepts[8].display_name | Meteorology |
| concepts[9].id | https://openalex.org/C121332964 |
| concepts[9].level | 0 |
| concepts[9].score | 0.19015005230903625 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q413 |
| concepts[9].display_name | Physics |
| concepts[10].id | https://openalex.org/C120665830 |
| concepts[10].level | 1 |
| concepts[10].score | 0.0971401035785675 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q14620 |
| concepts[10].display_name | Optics |
| concepts[11].id | https://openalex.org/C127313418 |
| concepts[11].level | 0 |
| concepts[11].score | 0.09270927309989929 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q1069 |
| concepts[11].display_name | Geology |
| concepts[12].id | https://openalex.org/C187320778 |
| concepts[12].level | 1 |
| concepts[12].score | 0.0 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q1349130 |
| concepts[12].display_name | Geotechnical engineering |
| concepts[13].id | https://openalex.org/C62520636 |
| concepts[13].level | 1 |
| concepts[13].score | 0.0 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q944 |
| concepts[13].display_name | Quantum mechanics |
| 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 |
| keywords[0].id | https://openalex.org/keywords/biomass |
| keywords[0].score | 0.6591202020645142 |
| keywords[0].display_name | Biomass (ecology) |
| keywords[1].id | https://openalex.org/keywords/water-content |
| keywords[1].score | 0.6428223252296448 |
| keywords[1].display_name | Water content |
| keywords[2].id | https://openalex.org/keywords/moisture |
| keywords[2].score | 0.5776764154434204 |
| keywords[2].display_name | Moisture |
| keywords[3].id | https://openalex.org/keywords/environmental-science |
| keywords[3].score | 0.555756151676178 |
| keywords[3].display_name | Environmental science |
| keywords[4].id | https://openalex.org/keywords/soil-science |
| keywords[4].score | 0.49802327156066895 |
| keywords[4].display_name | Soil science |
| keywords[5].id | https://openalex.org/keywords/detector |
| keywords[5].score | 0.4515109658241272 |
| keywords[5].display_name | Detector |
| keywords[6].id | https://openalex.org/keywords/hydrogen |
| keywords[6].score | 0.4300428628921509 |
| keywords[6].display_name | Hydrogen |
| keywords[7].id | https://openalex.org/keywords/agronomy |
| keywords[7].score | 0.23065125942230225 |
| keywords[7].display_name | Agronomy |
| keywords[8].id | https://openalex.org/keywords/meteorology |
| keywords[8].score | 0.22270655632019043 |
| keywords[8].display_name | Meteorology |
| keywords[9].id | https://openalex.org/keywords/physics |
| keywords[9].score | 0.19015005230903625 |
| keywords[9].display_name | Physics |
| keywords[10].id | https://openalex.org/keywords/optics |
| keywords[10].score | 0.0971401035785675 |
| keywords[10].display_name | Optics |
| keywords[11].id | https://openalex.org/keywords/geology |
| keywords[11].score | 0.09270927309989929 |
| keywords[11].display_name | Geology |
| language | en |
| locations[0].id | doi:10.1007/978-3-319-69539-6_4 |
| locations[0].is_oa | True |
| locations[0].source | |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | book-chapter |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent |
| locations[0].landing_page_url | https://doi.org/10.1007/978-3-319-69539-6_4 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5007805588 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-2947-0906 |
| authorships[0].author.display_name | Trenton E. Franz |
| authorships[0].countries | US |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I114395901 |
| authorships[0].affiliations[0].raw_affiliation_string | School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA |
| authorships[0].institutions[0].id | https://openalex.org/I114395901 |
| authorships[0].institutions[0].ror | https://ror.org/043mer456 |
| authorships[0].institutions[0].type | education |
| authorships[0].institutions[0].lineage | https://openalex.org/I114395901 |
| authorships[0].institutions[0].country_code | US |
| authorships[0].institutions[0].display_name | University of Nebraska–Lincoln |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | T. E. Franz |
| authorships[0].is_corresponding | True |
| authorships[0].raw_affiliation_strings | School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE, USA |
| authorships[1].author.id | https://openalex.org/A5036039712 |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Ammar Wahbi |
| authorships[1].affiliations[0].raw_affiliation_string | FAO/IAEA Division of Nuclear Techniques, Soil and Water Man & Crop, Nutrition Laboratory, Seibersdorf, Austria |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | A. Wahbi |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | FAO/IAEA Division of Nuclear Techniques, Soil and Water Man & Crop, Nutrition Laboratory, Seibersdorf, Austria |
| authorships[2].author.id | https://openalex.org/A5056074985 |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | W. A. Avery |
| authorships[2].countries | AT |
| authorships[2].affiliations[0].institution_ids | https://openalex.org/I1281029379 |
| authorships[2].affiliations[0].raw_affiliation_string | Soil and Water Management & Crop Nutrition Subprogramme, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency, Vienna, Austria |
| authorships[2].institutions[0].id | https://openalex.org/I1281029379 |
| authorships[2].institutions[0].ror | https://ror.org/02zt1gg83 |
| authorships[2].institutions[0].type | nonprofit |
| authorships[2].institutions[0].lineage | https://openalex.org/I1281029379 |
| authorships[2].institutions[0].country_code | AT |
| authorships[2].institutions[0].display_name | International Atomic Energy Agency |
| authorships[2].author_position | last |
| authorships[2].raw_author_name | W. Avery |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Soil and Water Management & Crop Nutrition Subprogramme, Joint FAO/IAEA Division of Nuclear Techniques in Food and Agriculture, International Atomic Energy Agency, Vienna, Austria |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Estimation of Biomass Water Equivalent via the Cosmic Ray Neutron Sensor |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11312 |
| primary_topic.field.id | https://openalex.org/fields/23 |
| primary_topic.field.display_name | Environmental Science |
| primary_topic.score | 0.9972000122070312 |
| 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 | Soil Moisture and Remote Sensing |
| related_works | https://openalex.org/W2108512031, https://openalex.org/W2063755384, https://openalex.org/W2153306223, https://openalex.org/W2997015357, https://openalex.org/W2048409327, https://openalex.org/W2390117385, https://openalex.org/W2897030841, https://openalex.org/W1977273908, https://openalex.org/W2079990215, https://openalex.org/W2123825282 |
| cited_by_count | 10 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 1 |
| counts_by_year[1].year | 2021 |
| counts_by_year[1].cited_by_count | 9 |
| locations_count | 1 |
| best_oa_location.id | doi:10.1007/978-3-319-69539-6_4 |
| best_oa_location.is_oa | True |
| best_oa_location.source | |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | book-chapter |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent |
| best_oa_location.landing_page_url | https://doi.org/10.1007/978-3-319-69539-6_4 |
| primary_location.id | doi:10.1007/978-3-319-69539-6_4 |
| primary_location.is_oa | True |
| primary_location.source | |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://link.springer.com/content/pdf/10.1007%2F978-3-319-69539-6_4.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | book-chapter |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Cosmic Ray Neutron Sensing: Estimation of Agricultural Crop Biomass Water Equivalent |
| primary_location.landing_page_url | https://doi.org/10.1007/978-3-319-69539-6_4 |
| publication_date | 2018-01-01 |
| publication_year | 2018 |
| referenced_works | https://openalex.org/W2132334553, https://openalex.org/W2126332365, https://openalex.org/W2288630212, https://openalex.org/W2142511814, https://openalex.org/W2135525724, https://openalex.org/W2097056051, https://openalex.org/W2022184765, https://openalex.org/W2486235249, https://openalex.org/W2604174897, https://openalex.org/W2466696963, https://openalex.org/W1487923163, https://openalex.org/W2130292253 |
| referenced_works_count | 12 |
| abstract_inverted_index.A | 40 |
| abstract_inverted_index.a | 9, 155, 186, 201 |
| abstract_inverted_index.~ | 22 |
| abstract_inverted_index.24 | 138 |
| abstract_inverted_index.As | 25 |
| abstract_inverted_index.an | 98, 275 |
| abstract_inverted_index.as | 8, 185, 200 |
| abstract_inverted_index.at | 3, 130 |
| abstract_inverted_index.be | 64, 161 |
| abstract_inverted_index.et | 68 |
| abstract_inverted_index.h) | 139 |
| abstract_inverted_index.if | 151 |
| abstract_inverted_index.in | 35, 97, 108, 144, 164, 178, 231 |
| abstract_inverted_index.is | 38, 75, 216, 270 |
| abstract_inverted_index.of | 11, 17, 20, 42, 54, 60, 79, 84, 105, 111, 134, 157, 169, 188, 238, 248, 264, 277 |
| abstract_inverted_index.on | 182, 245, 261 |
| abstract_inverted_index.so | 56 |
| abstract_inverted_index.to | 48, 81, 221, 250, 252 |
| abstract_inverted_index.0.5 | 265 |
| abstract_inverted_index.105 | 135 |
| abstract_inverted_index.250 | 23 |
| abstract_inverted_index.9]. | 210 |
| abstract_inverted_index.The | 0 |
| abstract_inverted_index.[6] | 273 |
| abstract_inverted_index.[7] | 70 |
| abstract_inverted_index.[8, | 209 |
| abstract_inverted_index.al. | 69 |
| abstract_inverted_index.and | 50, 122, 140, 172, 219, 228, 255, 274 |
| abstract_inverted_index.can | 63 |
| abstract_inverted_index.for | 137, 203 |
| abstract_inverted_index.its | 4, 15, 183, 198 |
| abstract_inverted_index.m). | 24 |
| abstract_inverted_index.not | 128 |
| abstract_inverted_index.the | 12, 32, 36, 58, 76, 82, 85, 101, 109, 145, 170, 176, 213, 224, 232, 246, 262, 268 |
| abstract_inverted_index.use | 184, 199, 237 |
| abstract_inverted_index.°C | 136 |
| abstract_inverted_index.5000 | 249 |
| abstract_inverted_index.CRNS | 1, 86, 177 |
| abstract_inverted_index.Much | 168 |
| abstract_inverted_index.Note | 211 |
| abstract_inverted_index.This | 235 |
| abstract_inverted_index.also | 194 |
| abstract_inverted_index.area | 16, 276 |
| abstract_inverted_index.been | 46, 195 |
| abstract_inverted_index.crop | 206 |
| abstract_inverted_index.data | 94 |
| abstract_inverted_index.form | 110 |
| abstract_inverted_index.from | 141, 223 |
| abstract_inverted_index.have | 45, 193 |
| abstract_inverted_index.high | 242 |
| abstract_inverted_index.into | 119, 175, 197 |
| abstract_inverted_index.left | 152 |
| abstract_inverted_index.most | 5, 102 |
| abstract_inverted_index.must | 160 |
| abstract_inverted_index.only | 91 |
| abstract_inverted_index.oven | 131 |
| abstract_inverted_index.past | 173 |
| abstract_inverted_index.show | 95 |
| abstract_inverted_index.soil | 37, 61, 73, 112, 189, 225 |
| abstract_inverted_index.than | 29 |
| abstract_inverted_index.that | 30, 57, 72, 96, 159, 212 |
| abstract_inverted_index.tool | 202 |
| abstract_inverted_index.with | 87 |
| abstract_inverted_index.These | 93 |
| abstract_inverted_index.bound | 123 |
| abstract_inverted_index.comes | 107 |
| abstract_inverted_index.count | 243 |
| abstract_inverted_index.error | 106 |
| abstract_inverted_index.large | 239 |
| abstract_inverted_index.level | 7 |
| abstract_inverted_index.noise | 230 |
| abstract_inverted_index.order | 247, 263 |
| abstract_inverted_index.other | 28 |
| abstract_inverted_index.rates | 244 |
| abstract_inverted_index.small | 218 |
| abstract_inverted_index.sound | 272 |
| abstract_inverted_index.such, | 26 |
| abstract_inverted_index.there | 192 |
| abstract_inverted_index.these | 52 |
| abstract_inverted_index.this, | 148 |
| abstract_inverted_index.vapor | 143 |
| abstract_inverted_index.water | 33, 114, 124, 142 |
| abstract_inverted_index.(i.e., | 115, 241, 260 |
| abstract_inverted_index.10,000 | 251 |
| abstract_inverted_index.active | 278 |
| abstract_inverted_index.crops. | 167 |
| abstract_inverted_index.drying | 132 |
| abstract_inverted_index.fairly | 217 |
| abstract_inverted_index.grains | 127 |
| abstract_inverted_index.itself | 208 |
| abstract_inverted_index.limits | 259 |
| abstract_inverted_index.partly | 163 |
| abstract_inverted_index.radius | 21 |
| abstract_inverted_index.remove | 222 |
| abstract_inverted_index.sensor | 187 |
| abstract_inverted_index.series | 41 |
| abstract_inverted_index.signal | 59, 83, 215, 227 |
| abstract_inverted_index.source | 104, 156 |
| abstract_inverted_index.within | 14, 31 |
| abstract_inverted_index.(circle | 19 |
| abstract_inverted_index.Despite | 147 |
| abstract_inverted_index.between | 125 |
| abstract_inverted_index.biomass | 89, 150, 207, 214, 257 |
| abstract_inverted_index.certain | 256 |
| abstract_inverted_index.counts. | 234 |
| abstract_inverted_index.current | 171 |
| abstract_inverted_index.focuses | 181 |
| abstract_inverted_index.growing | 88, 149, 205 |
| abstract_inverted_index.kg/m2). | 266 |
| abstract_inverted_index.largest | 77 |
| abstract_inverted_index.lattice | 113 |
| abstract_inverted_index.mineral | 120, 126 |
| abstract_inverted_index.neutron | 233 |
| abstract_inverted_index.remains | 154 |
| abstract_inverted_index.sources | 53 |
| abstract_inverted_index.However, | 191 |
| abstract_inverted_index.McJannet | 67 |
| abstract_inverted_index.[1–6]. | 66 |
| abstract_inverted_index.detector | 10 |
| abstract_inverted_index.hydrogen | 13, 27, 55, 80, 116 |
| abstract_inverted_index.inherent | 229 |
| abstract_inverted_index.isolated | 65 |
| abstract_inverted_index.minimize | 253 |
| abstract_inverted_index.moisture | 62, 74, 226 |
| abstract_inverted_index.quantify | 49 |
| abstract_inverted_index.released | 129 |
| abstract_inverted_index.requires | 236 |
| abstract_inverted_index.research | 174 |
| abstract_inverted_index.detected. | 39 |
| abstract_inverted_index.detection | 258 |
| abstract_inverted_index.detectors | 240 |
| abstract_inverted_index.developed | 47 |
| abstract_inverted_index.eliminate | 51 |
| abstract_inverted_index.equations | 44 |
| abstract_inverted_index.functions | 2 |
| abstract_inverted_index.influence | 18 |
| abstract_inverted_index.moisture. | 190 |
| abstract_inverted_index.molecules | 34, 117 |
| abstract_inverted_index.research. | 279 |
| abstract_inverted_index.slightly. | 92 |
| abstract_inverted_index.technique | 269 |
| abstract_inverted_index.addressed, | 162 |
| abstract_inverted_index.estimating | 204 |
| abstract_inverted_index.integrated | 118 |
| abstract_inverted_index.structures | 121 |
| abstract_inverted_index.atmosphere. | 146 |
| abstract_inverted_index.calibration | 43 |
| abstract_inverted_index.challenging | 220 |
| abstract_inverted_index.contributor | 78 |
| abstract_inverted_index.fundamental | 6 |
| abstract_inverted_index.significant | 103 |
| abstract_inverted_index.uncertainty | 158 |
| abstract_inverted_index.Nevertheless | 267 |
| abstract_inverted_index.agricultural | 99, 166, 179 |
| abstract_inverted_index.contributing | 90 |
| abstract_inverted_index.demonstrated | 71 |
| abstract_inverted_index.environment, | 100 |
| abstract_inverted_index.environments | 180 |
| abstract_inverted_index.fast-growing | 165 |
| abstract_inverted_index.temperatures | 133 |
| abstract_inverted_index.uncertainty) | 254 |
| abstract_inverted_index.unquantified | 153 |
| abstract_inverted_index.theoretically | 271 |
| abstract_inverted_index.investigations | 196 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5007805588 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| corresponding_institution_ids | https://openalex.org/I114395901 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/2 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Zero hunger |
| citation_normalized_percentile.value | 0.95343015 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |