Effect of milling treatments and storage conditions on the dehulling characteristics of red lentils
Why this work is in the frame
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Bibliographic record
Abstract
Milling is an important process in the post-harvest handling of red lentils. The effect of milling moisture content, milling speed and milling time on the dehulling characteristics of two varieties of red lentils, CDC Impact and CDC Redberry, was investigated. Response surface methodology with a central composite design was used to determine optimum milling conditions. Optimum milling conditions included: milling moisture content of 12.5%, milling speed of 1,100 RPM and dehulling time of 38 s. Milling with these parameters resulted in dehulling efficiencies of ≥ 85%. Various treatments (high heat drying, near ambient drying, and successive freezing and thawing cycles) were employed to simulate different storage conditions. Dehulling efficiency of both red lentil varieties was negatively affected by drying yet dehulling quality was not adversely affected by freezing and thawing. This work provides information necessary to establish post-harvest techniques that maximise the dehulling efficiency of red lentils.
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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