Classification, Force Deformation Characteristics and Cooking Kinetics of Common Beans
Bibliographic record
Abstract
Post-harvest characteristics of common beans influences its classification, which significantly affects processing time and energy requirements. In this work, ten bean cultivars were classified as either easy-to-cook (ETC) or hard-to-cook (HTC) based on a traditional subjective finger pressing test and a scientific objective hardness test. The hardness study used seed coat rigidity to explain the structural deformation observed during cooking. The result shows that the average hardness of raw dry ETC and HTC beans was 102.4 and 170.8 N, respectively. The maximum seed coat resistance is observed within the first 30 min of cooking regardless of the classification. The results show that a modified three-parameter non-linear regression model could accurately predict the rate of bean softening (R2 = 0.994–0.999 and RMSE = 3.3–14.7%). The influence of bean softeners such as potassium carbonate (K2CO3) and sodium chloride (NaCl) to reduce cooking time was also investigated. The results showed that the addition of K2CO3 to the cooking water significantly reduced the cooking time by up to 50% for ETC and 57% for HTC.
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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 | 0.001 | 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".