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Record W4225783004 · doi:10.1109/jstars.2021.3124743

Validation of Soil Moisture Data Products From the NASA SMAP Mission

2021· article· en· W4225783004 on OpenAlexafffund
Andreas Colliander, Rolf H. Reichle, Wade T. Crow, Michael H. Cosh, Fan Chen, S. Chan, Narendra N. Das, Rajat Bindlish, Julián Chaubell, Seung-Bum Kim, Qing Liu, Peggy O’Neill, R. S. Dunbar, Land B. Dang, John S. Kimball, Thomas J. Jackson, Hala Khalid Al-Jassar, Jun Asanuma, Bimal K. Bhattacharya, Aaron Berg, David D. Bosch, Laura Bourgeau‐Chavez, Todd G. Caldwell, Jean‐Christophe Calvet, Chandra Holifield Collins, Karsten H. Jensen, Stan Livingston, Ernesto López-Baeza, José Martínez‐Fernández, Heather McNairn, Mahta Moghaddam, Carsten Montzka, Claudia Notarnicola, Thierry Pellarin, Isabella Pfeil, Jouni Pulliainen, Judith Ramos, M. S. Seyfried, Patrick J. Starks, Zhongbo Su, R. van der Velde, Yijian Zeng, M. Thibeault, Mariëtte Vreugdenhil, Jeffrey P. Walker, Mehrez Zribi, Dara Entekhabi, Simon Yueh

Bibliographic record

VenueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Moisture and Remote Sensing
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
FundersCenter for Neuroscience and Regenerative MedicineInstitut National de Recherche pour l'Agriculture, l'Alimentation et l'EnvironnementAgriculture and Agri-Food CanadaUniversidad de SalamancaComisión Nacional de Actividades EspacialesUniversidad Nacional Autónoma de MéxicoCentre National de la Recherche ScientifiqueJet Propulsion LaboratoryCentre National d’Etudes SpatialesUniversité de ToulouseUniversitat de ValènciaUniversity of TwenteMinisterio de Ciencia, Innovación y UniversidadesMonash UniversityUniversity of Texas at AustinCopenhagen Graduate School for Nanoscience and NanotechnologyNational Aeronautics and Space AdministrationU.S. Department of AgricultureEuropean Regional Development FundCalifornia Institute of TechnologyMassachusetts Institute of TechnologyAgricultural Research ServiceUniversity of Southern California
KeywordsEnvironmental scienceRemote sensingMoistureWater contentAstrobiologyMeteorologyGeology

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.241
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations181
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing→Same topicSoil Moisture and Remote Sensing→French-language works237,207→