Quels leviers techniques pour l'atténuation des émissions de gaz à effet de serre d'origine agricole ?
Bibliographic record
Abstract
About 20% of French emissions of greenhouse gases (GHG) originate from the agricultural sector. Ten technical measures, split into 26 sub-measures, were proposed to reduce GHG emissions from French agriculture over the period 2010-2030. Their abatement potential and cost for the farmer were compared. The proposed measures were linked to nitrogen management, management practices which increase carbon storage in soils and biomass, livestock diets and energy production and consumption on farms. The overall abatement potential can be broken down into three parts. One third of the cumulated abatement potential corresponds to sub-measures with a negative technical cost. These submeasures are based on an improved efficiency of inputs like N fertilizers, animal feed and energy. The second part corresponds to sub-measures with a moderate cost (<€25 per metric ton of CO2e avoided). These sub-measures require specific investments or modifying the cropping system slightly more, but additional costs or lower incomes are partially compensated for by a reduction in other costs or additional marketable products. The third part corresponds to sub-measures with a high cost (>€25 per metric ton of CO2e avoided). These sub-measures require investment with no direct financial return, the purchase of specific inputs, dedicated labour time or involve greater production losses.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".