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Record W4297145753 · doi:10.1111/1365-2745.13997

Plant litter chemistry controls coarse‐textured soil carbon dynamics

2022· article· en· W4297145753 on OpenAlexafffund
Raoul Huys, Vincent Poirier, Malo Y. Bourget, Catherine Roumet, Stephan Hättenschwiler, Nathalie Fromin, Alison D. Munson, Grégoire T. Freschet

Bibliographic record

VenueJournal of Ecology · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil Carbon and Nitrogen Dynamics
Canadian institutionsUniversité LavalUniversité du Québec en Abitibi-Témiscamingue
FundersFondation de l’Université du Québec en Abitibi-TémiscamingueLabexAgence Nationale de la RechercheNatural Sciences and Engineering Research Council of CanadaCentre Méditerranéen de l’Environnement et de la Biodiversité
KeywordsMineralization (soil science)ChemistryPlant litterSoil carbonSoil waterEnvironmental chemistrySoil organic matterOrganic matterNitrogen cycleLitterAgronomyNitrogenSoil scienceNutrientEnvironmental scienceBiologyOrganic chemistry

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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.591
Threshold uncertainty score0.878

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.189
Teacher spread0.181 · 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.

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

Citations64
Published2022
Admission routes2
Has abstractyes

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