Evaluation of the Drip Loss of 30 Cultivars and 9 Advanced Selections from Agriculture and Agri-Food Canada National Strawberry Breeding Program
Bibliographic record
Abstract
Fruits of thirty-nine strawberry genotypes were evaluated for their freezing performance based on their drip loss percentage. The amount juice lost was evaluated for each genotype after four months of storage (at — 20°C) upon thawing at 20°C for 20 hr. A preliminary selection based on the drip loss method or exudation enabled us to eliminate genotypes that are the least interesting from a freezing standpoint and to focus our efforts on those with a high processing potential. ‘NY1529’, ‘Scott’, ‘Arking’, ‘SJ8317-5’ and ‘SJ83145-1’ with less than 30% juice loss seems suitable for jam, yogurt and frozen food production. On the other hand, with more than 60% juice loss, ‘Tenira’, ‘Primela’ and ‘Splendida’ seem less desirable for processing.
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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 | 0.000 |
| 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) | 0.001 | 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".