Comment articuler modes d'évaluation des variétés, conduite des cultures et processus d'amélioration génétique ?
Bibliographic record
Abstract
The joint and consistent development, during the last decades, of cultivar breeding programmes aiming at improving grain yield and of innovative techniques lifting limiting factors in the fields led to a significant and regular increase of the national crop production, in many species. In front of the global changes, stakes linked to sustainable development, and diversification of requirements from market and society, it is necessary to change farming systems. Given the coherence of farming systems, and given the strong link between cultivar breeding and crop management, cultivar evaluation should take into account the higher diversity of growing conditions and, consequently, of the criteria used for evaluation. In addition, the breeding process should also integrate this diversity and the time-course change of the socio-technical systems. We are therefore invited to a real paradigm change! If the overall framework for such a global evolution is still to be implemented, numerous tools, particularly modelling, are already available and could efficiently complement the current experimental networks for breeding and cultivar evaluation.
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 | 0.049 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.017 | 0.029 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".