Peut-on mesurer les conséquences du retrait d’une molécule herbicide pivot en s’appuyant sur les expériences passées ?
Bibliographic record
Abstract
Withdrawal of herbicides active ingredients already started in 2003 in European Union, will probably lead to increasingly strong agronomical effects. The ban of ‘keystone’ active ingredients requires in-depth adaptations especially linked with the cropping system. For five years, glyphosate made big headlines in France and in Europe. Widely used for its weeding action during the intercropping period, alternatives to glyphosate will certainly rely on a combination of different additive cultural practices. However, it is not the first withdrawal of a major active ingredient. In 2003, the ban of atrazine, which was the main chemical ingredient for maize weeding, had already been a major issue of concern for the agricultural sector. Alternatives, mainly chemical, enabled farmers to deal rapidly with the atrazine ban. However, glyphosate ban appears to be more challenging and a deeper reconsideration of cropping systems will be certainly needed. Moreover, other chemical ingredients are soon to be withdrawn and new other ‘crises’ may arise in a next future, with more and more complex alternative solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".