Water sorption and cooking time of red kidney beans (<i>Phaseolus vulgaris</i> L.): part I – Effect of freezing and drying conditions on water sorption and cooking time
Bibliographic record
Abstract
Abstract Water sorption and cooking time of kidney beans were determined. The beans were manually harvested at 19.2 ± 0.1% moisture content and stored at −20 and −10 °C for about half a year. The beans were further dried at 30, 40 and 50 °C inside a thin-layer drier for 7.5 h or under room conditions for 4 week. The freezing storage temperature before the beans were dried did not influence their cooking time and water sorption. The saturated kernel volumes decreased approximately 7% after drying. The beans decreased their sphericity during water sorption and had a larger swelling ratio in the thickness direction than in other directions. Lower initial moisture content, especially with a higher drying temperature, decreased water sorptivity and resulted in higher percentage of uncooked kernels if the beans were not soaked before cooking. However, there was no relationship between initial moisture content and uncooked percentage if the beans were soaked before cooking. High drying temperature resulted in hard-to-cook (HTC) phenomenon.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".