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Record W2613538282 · doi:10.4267/2042/62171

Observation de la végétation depuis l'espace

2017· article· fr· W2613538282 on OpenAlexaff
Jean‐Christophe Calvet, Éric Ceschia, Dominique Courault, Hélène Dewaele, Yves Goulas, Jordi Inglada, Thuy Le Toan, Fabienne Maignan

Bibliographic record

VenueLa Météorologie · 2017
Typearticle
Languagefr
FieldEnvironmental Science
TopicRemote Sensing and LiDAR Applications
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsForestryHumanitiesPhysicsGeographyArt

Abstract

fetched live from OpenAlex

L'arrivée de nouvelles données satellitaires en accès libre (notamment du programme européen Sentinel de Copernicus) fait avancer la caractérisation de l'occupation des sols et des cycles de l'eau et du carbone. La résolution spatiale décamétrique de ces données, disponibles à une fréquence élevée, permet de produire des variables biogéophysiques à l'échelle des parcelles agricoles. Des applications en agrométéorologie sont possibles, mais également pour la validation et l'amélioration des modèles des surfaces terrestres utilisés en météorologie et en climat. Deux nouvelles missions de l'Agence spatiale européenne, Biomass et Flex, dont les lancements sont programmés respectivement pour 2021 et 2022, vont apporter des connaissances nouvelles sur la photosynthèse et le stockage de carbone par les forêts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.741
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.032
GPT teacher head0.309
Teacher spread0.276 · 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 teacher head, not a consensus.

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

Citations0
Published2017
Admission routes1
Has abstractyes

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