Firmness, Respiration, and Weight Loss of `Bing', `Lapins' and `Sweetheart' Cherries in Relation to Fruit Maturity and Susceptibility to Surface Pitting
Bibliographic record
Abstract
A convenient and reliable method that used a specially designed tool to apply a uniform bruising force in situ was developed to assess the relative susceptibility to fruit surface pitting in sweet cherry. Assessment of pitting with a visual scale after 2 weeks of 1 °C storage was found to be in close agreement with measurements of pit diameter. Using this method `Bing' showed the greatest susceptibility to pitting in both years of the study and `Bing', `Lapins', and `Sweetheart' cherries showed a decline in susceptibility as fruit matured. The predictive value of fruit firmness at harvest, fruit respiration at harvest, and weight loss in storage was assessed in relation to the severity of pitting. The model to best describe pitting was found to include all three physiological variables (firmness, respiration, and weight loss). While an acceptable model was obtained when combining all three cultivars, the best models were achieved when each cultivar was considered separately. It was concluded that there are likely unmeasured variables involved in determining susceptibility to pitting. Hence the best approach to predicting pitting susceptibility is the application of the pit-induction method described in this work.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".