Potentiel d’atténuation des changements climatiques par les couverts intermédiaires.
Bibliographic record
Abstract
Cover crops have long been used for their ability to reduce erosion, reduce nitrate leaching and improve soil properties in general. Since the application of the European directive on nitrate in vulnerable zones, the use of cover crops has increased and, in a climate change context, there is a growing interest for their ability to increase soil organic carbon content. This practice is considered as fundamental in the “4 per 1000” initiative that was launched after the COP21. In this paper, we evaluate the potential of cover crops to increase carbon storage in agricultural soils but also the positive and negative effects of cover crops on climate. We consider both the biogeochemical effects (Carbon storage in soil, N2O emissions, emissions from field operations) and biophysical effects (changes in albedo and energy balance at soil surface) that modify the radiative forcing (net climatic effect) of plots where cover crops are grown in comparison with bare fallow plots. This innovative integrated approach is based on recent literature, ongoing research and meta-analysis. The synthesis of these studies shows that in most cases there is a synergy between the biogeochemical cooling effects and the biophysical ones. A more systematic accounting of all those processes could increase the climate mitigation efficiency of cover crops.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
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".