Effects of Preharvest Weather Conditions on Firmness of ‘McIntosh’ Apples at Harvest Time
Bibliographic record
Abstract
Apple fruit firmness is one of the main attributes indicating fruit quality at harvest. It is affected by numerous factors during the entire growing season. The effects of weather conditions during apple development are often mentioned as a result of their impact on attributes linked to fruit firmness: fruit size, calcium concentration, water content, etc. In this study, the effects of weather conditions on ‘McIntosh’ apple ( Malus × domestica Borkh. cv. McIntosh) firmness at harvest time were analyzed. Fruit were harvested at nine sites in Quebec and Ontario over 15 years (1996–2011). For each case, weather parameters were analyzed from full bloom until harvest, either in monthly subperiods from May until September or in terms of days from full bloom (DFB) until harvest. Regression results highlighted the negative effect of lower air temperature conditions from 31 to 60 DFB, higher air temperature conditions and precipitations from 61 to 90 DFB, and higher temperature conditions from 91 DFB until harvest on ‘McIntosh’ apple firmness level at harvest. Precipitation from 61 to 90 DFB alone explained 39% of ‘McIntosh’ apple firmness variation at harvest time. The prediction of apple firmness at harvest time could be helpful for producers to adjust their marketing and storage strategies according to apple quality level.
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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 | 0.000 | 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".