The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols
Bibliographic record
Abstract
Climate change is expected to alter a multitude of factors important to agricultural systems. Changes to carbon dioxide concentrations ([CO2]), temperature and water (CTW) will be the primary drivers of climate change in crop growth and agricultural systems. Therefore, establishing the CTW-chnage sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying enviromental conditions and farm management techniques, and have been applied in climate change assessment by using a variety of methods. However, there is a significant amount of divergence between various crop models' responses to CTW changes. While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts. \nTo fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP). The submitted results from C3MP Phase I are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.047 |
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 source (direct Gemma or distilled Codex), 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".