Et demain ? Robustesse des stratégies innovantes de gestion des adventices face au changement climatique.
Bibliographic record
Abstract
To evaluate the resilience of innovative weed management strategies, the impact of climate change was investigated with a regionalized simulation approach. First, we checked that the protocol for dynamic regionalisation correctly mimicked the climate observed over 1980-2016. Then, the protocol was applied to two periods (2010-2040 and 2069-2099). Finally, we compared existing and prospective cropping systems in terms of weed management and impacts on crop production and biodiversity, with these two climate trajectories. To do so, 44 cropping systems from 5 regions were simulated with the weed dynamics model FlorSys. Our results show that surface temperature will increase in average by approx. 4°C at the end of the century with the RCP8.5 trajectory. Three cropping system types were identified in response to these climate changes, those whose weed harmfulness for crop production (1) decreased, (2) increased, particularly with trajectory RCP8.5 from 2069-2099, (3) was average but whose weed contribution to biodiversity increased. The study allowed identifying the management levers essential for innovative cropping systems that are robust relatively to climate change.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".