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Record W7139958713 · doi:10.57935/agr.29486213

Experimental and simulated data for crop and grassland production and carbon-nitrogen fluxes

2025· article· W7139958713 on OpenAlexaboutno aff
Renáta Sándor, Fiona Ehrhardt, Peter Grace, Sylvie Recous, Pete Smith, Val Snow, Jean-François Soussana, Bruno Bartelle Basso, Arti Bhatia, Lorenzo Brilli, Jordi Doltra, Christopher D. Dorich, Luca Doro, Nuala Fitton, Brian Grant, Matthew Tom Harrison, Miko Kirschbaum, Katja Klumpp, Patricia Laville, Joël Léonard, Raphaël Martin, Raia Silvia Massad, Andrew Moore, Vasileios Myrgiotis, Elizabeth Pattey, Susanne Rolinski, Joanna Sharp, Ute Skiba, Ward Smith, Lianhai Wu, Qing Zhang, Gianni Bellocchi

Bibliographic record

VenueAgResearch · 2025
Typearticle
Language
FieldAgricultural and Biological Sciences
TopicClimate change impacts on agriculture
Canadian institutionsnot available
Fundersnot available
KeywordsGrasslandPrimary productionGrassland ecosystemCropEcosystemStanding cropEcosystem modelGrowing season

Abstract

fetched live from OpenAlex

Multi-year datasets from field experiments and simulations at five agricultural sites in the Northern Hemisphere were developed for three cropland sites in Ottawa (Canada), Grignon (France) and Delhi (India) and two grassland sites at Laqueuille (France) and Easter Bush (UK). The cropland sites have rotations with wheat, triticale, maize, rapeseed, soybean, phacelia and rice, as well as periods of bare fallow. Cattle (Laqueuille) or mixed cattle and sheep (Easter Bush) graze in the two grassland sites. Field data were collected between 2003 and 2012 for three to eight years, including grain yield/above‐ground net primary productivity, nitrous oxide emissions, carbon fluxes (gross primary production, net ecosystem exchange, ecosystem respiration), together with daily weather data (solar radiation, maximum and minimum temperatures, precipitation, wind speed, relative humidity, vapour pressure), soil properties, and records of crop and grassland management. Simulated outputs are from 23 models: 11 crop models, eight grassland models and four models simulating both systems.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.710
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.111
GPT teacher head0.374
Teacher spread0.263 · 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 designBench or experimental
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
Published2025
Admission routes1
Has abstractyes

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