The effect of dry-ice freezing on Saskatoon berry quality
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Bibliographic record
Abstract
Yakimishen, R., Cenkowski, S. and Muir, W.E. 2002. The effect of dry-ice freezing on saskatoon berry quality. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 44:3.17-3.25. A batch freezing system was used in circulating sublimated carbon dioxide within a closed loop column to quick-freeze saskatoon berries (Amelanchier alnifolia). The amount of dry-ice used, gas velocities, and final berry quality were measured. Consumption ratios of dry-ice to mass of berries ranged from 1.25 to 2.69 kg/kg. The freezing times of fresh Smoky and Thiessen berries (700 g samples) varied from 205 to 265 s and 220 to 275 s, respectively to cool and freeze from approximately 20 to −15°C. Quality was measured by quantifying colour, anthocyanin content, benzaldehyde content, total acidity (malic acid), and soluble solids (sugars) in frozen Smoky and Thiessen berries. Colour was evaluated using ‘L’, ‘a’, and ‘b’ values and ranged from 14.9 to 21.4, 0.4 to 10.5, and −3.7 to 2.8, respectively, which related to the brightness, redness, and blueness, respectively, of Smoky berries. Anthocyanin content (red/blue colour characteristic) ranged from 113.3 to 309.2 ppm and benzaldehyde values (aroma) ranged from 6.4 to 14.0 ppm. Total acidity was expressed as percent malic acid and was found to be between 0.32 and 0.39. Soluble solids was expressed as percent sucrose and ranged between 3.0 and 13.9. Quality results were dependent on the date of harvest or berry maturity and the number of days the berries were held in storage before quality tests were conducted. Experimental results showed quick-freezing using dry-ice to be effective in preserving the quality of freshly harvested saskatoon berries.
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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