Global Research Alliance on Agricultural Greenhouse Gases - benchmark and ensemble crop and grassland model estimates
Bibliographic record
Abstract
The Soil Carbon and Nitrogen Cycling Cross-cutting Group (Soil CN group) of the Global Research Alliance on Agricultural Greenhouse gases (GRA) promotes a coordinated activity across multiple international projects (e.g. CN MIP and Models4Pastures of the FACCE-JPI https://www.faccejpi.com) to benchmark and compare simulation models that simulate GHG emissions from arable crop and grassland systems. Ten long-term experimental sites are studied covering a variety of climatic and geographic conditions worldwide (Australia, Brazil, Canada, France, India, New Zealand, Switzerland, United Kingdom and United States). Twenty-four process-based models of different complexity have contributed to the modelling exercise in different stages, each with access to gradually more detailed data to run and evaluate models of a multi-stage protocol. We present a comparison of model estimates of production (e.g. grain yield, gross primary production, above-ground net primary production, grassland grazing or defoliation) and vegetation (e.g. leaf area index) outputs, as well as GHG emissions (e.g. ecosystem respiration, nitrous oxide, enteric methane) from individual models to the multi-model ensemble. We found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of crop and grassland processes. We show that uncertainties are considerably reduced after calibration with detailed production and phenology data. The multi-model approach also allowed for improved performance, according to relative root mean square error and relative bias performance metrics. Calibrated models provide a reliable basis for testing mitigation options at the studied sites.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".