Connaissance, adaptation et amélioration de la gestion quantitative de l’eau avec des collectifs d’irrigants de Midi-Pyrénées.
Bibliographic record
Abstract
The work carried out with three irrigation associations of the Midi-Pyrénées area made it possible to improve methods in the technical, organisational and financial audit of those structures. Irrigation water efficiency could be measured on three irrigation networks as well as in some farms using rain gun and sprinkler systems. The adaptation of cropping plan for irrigated farms to changes in the regulatory, economic and climatic context has been analysed with farmers using the simulator LORA. In the framework of this project, the tool was upgraded and the production functions « crop yield/water consumption » of the main species were updated with recent experimental data, especially on maize, sorghum and sunflower. CRASH, a dynamic model aiming at helping the decisional process regarding cropping plan was designed during a PhD thesis. For irrigation management, the project set the scene and proposed progress in methods for accompanying collective and individual management of water resources during the irrigation season. To propose strategies for irrigation management by crop adapted to each water resources context, a generic approach to build a simulator already implemented on maize was used on sunflower and durum wheat. Irrigation decision models were designed and biophysical models were parameterized, SUNFLO for sunflower and STICS for durum wheat.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".