Évolution des calibres et des rendements de cultivars de haricot destinés à la transformation
Bibliographic record
Abstract
The evolution of sieve-size, yield and quality are all factors that contribute to the determination of an optimal harvest date for snap beans (Phaseolus vulgaris L.) destined for processing. The evolution of two of these parameters was evaluated in two cultivars of processing beans (Applause and Goldmine) over seven harvesting dates from 1996 to 1997. Three sieve size groups were compared: Small (P, categories 1, 2, 3), Medium (M, category 4) and Large (G, categories 5, 6). At the optimal harvest date, Goldmine had a significantly higher yield in 1997 (17.6 t ha−1) than in 1996 (10.9 t ha−1), whereas no significant difference was found for Applause. Total yields increased at a constant rate (13.2% d−1 for Goldmine and 8.4% d−1 for Applause) (weight for weight), which did not vary year to year. Sieve size evolution was generally linear except for size P in Applause, which exhibited a curvilinear pattern. The rate of change in sieve size varied from year to year, except in the case of size G in Goldmine. Key words: harvest, maturity, planning, Phaseolus vulgaris, quality
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".