Effets de quelques pratiques horticoles de conduite des plantes sur la production de roses coupées
Bibliographic record
Abstract
Four different plant management treatments, corresponding with different existing growing practices, were compared for flower production of Rosa hybrida L. cv. Meiqualis plants grown for 1 year in a greenhouse in the south of France. Two of them used shoot bending during plant formation and subsequent production management. The harvest level practiced on the thickest stems differentiated these two treatments. Two other treatments consisted in using shoot removal and pruning during plant formation. In the first one, during production management, unmarketable shoots were removed by pinching, whereas they were bent and left on the plant in the second one. This last treatment resulted in the highest yield of marketable stems and stem quality after 1 year, although the treatment using shoot bending during plant formation and production associated with a high harvest level on the thickest stems produced the largest quantity during autumn and winter. The results are discussed with reference to different yield components determined by the different plant management treatments applied, such as inter-flush cycle time, bud sprouting, occurrence of certain types of unmarketable stems (blind shoots and bowed peduncled stems), and development of new bottom breaks.Key words: shoot bending, bottom breaks, blind shoots, bowed stems, production, Rosa hybrida L.
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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 | 0.000 |
| 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 | 0.001 | 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".