Réseau ECOHERBMIP : Faisabilité, performance et durabilité de systèmes de cultures économes en herbicides.
Bibliographic record
Abstract
An experiment of cropping systems was led by Arvalis-Institut du vegetal in partnership with Terres Inovia and ACTA for 9 years, from 2010 to 2018, in the context of the Lauragais clay-limestone soil. Compared to 2 "conventional" systems in short rotation with or without ploughing, 3 cropping systems with low herbicides input including 1 with diversification of the rotation and cover crop during intercropping period, have been evaluated; They include different agronomic practices (ploughing, fasten seedbed, shift of sowing,...) and alternative weeding technics (harrow, hoeing, localized chemical weeding). This platform was completed and enriched from 2012 by 4 observatories by the departmental agriculture chambers 31, 32, 81 and 82 on which 2 low herbicides input systems have been tested in comparison to a control system. The results allow to characterize the impact of systems on the weed evolution. Among the various levers implemented to reduce herbicide dependence, the best performers were tillage (more specifically plowing), lengthening and diversification of crop rotation, shift of sowing date and mechanical weeding (especially hoeing and, if possible, associated with chemical weed control on the sowing line). The performance involves a combination of these levers and therefore requires a redesign of the culture system. A multicriteria analysis on the studied cropping systems with the tool Systerre® was realized to calculate economical, technical and environmental indicators. It illustrates, among other things, a certain economic fragility of systems integrating diversification cultures.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".