Effets du travail du sol et de la gestion des résidus sur les propriétés du sol et sur l’érosion hydrique d'un Vertisol Méditerranéen
Bibliographic record
Abstract
Moussadek, R., Mrabet, R., Zante, P., Lamachère, J. M., Pépin, Y., Le Bissonnais, Y., Ye, L., Verdoodt, A. and Van Ranst, E. 2011. Impact of tillage and residue management on the soil properties and water erosion of a Mediterranean Vertisol. Can. J. Soil Sci. 91: 627–635. Soil erosion research on Mediterranean Vertisols under no tillage systems (NT) is still scarce. A rainfall simulator was used on Vertisols to compare water runoff and soil loss in a conventional tillage system (CT), NT system with crop residues removed (NT0), and NT with 50% of crop residues returned to the soil surface (NT50). Runoff and soil loss rates were more than 50% lower under NT50 compared with NT0 and CT. Wet aggregate stability (MWD), soil organic matter (SOM) and soil bulk density (Da) were significantly higher under NT than under CT. A multiple regression analysis showed that when the soil was dry, Da explained 84 and 96% of the variation in water runoff and soil loss, respectively. Under wet soil conditions, MWD explained 47 and 69% of variation in water runoff and soil loss, respectively. Consequently, although NT systems improved soil quality (MWD, SOM) compared with the CT system, returning 50% of crop residues at the soil surface was mandatory under NT to protect these Vertisols against water erosion.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".