Evaluation of the Drip Loss of 30 Cultivars and 9 Advanced Selections from Agriculture and Agri-Food Canada National Strawberry Breeding Program
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it