Optimization of a Laboratory Dehulling Process for Lentil (<i>Lens culinaris</i>)
Bibliographic record
Abstract
ABSTRACT Red lentil (Lens culinaris) is mainly processed into dehulled and split forms before human consumption and characteristics such as dehulling efficiency (DE), which is the sum of percent dehulled whole seed (PDW) and percent dehulled split seed (PDS), are important to lentil breeders, processors, and exporters. A laboratory Satake dehuller was used to evaluate the dehulling characteristics of red lentil. The effects of dehulling conditions (abrasive wheel speed, dehulling time, and seed moisture content) were investigated using response surface methodology. Increasing dehulling time and seed moisture content decreased DE. Increasing seed moisture content decreased powder and broken fractions but increased the undehulled whole seed fraction. PDW was decreased but PDS was increased as dehulling time was increased. Percent hull removed during dehulling process decreased as seed moisture content was increased but increased as abrasive wheel speed or dehulling time was increased. The optimum dehulling conditions for the laboratory dehuller, based on maximizing DE and percent hull removed while minimizing powder fraction (loss), were established. Good agreement was found between experimental values for dehulling characteristics (DE, PDW, PDS, hull removed, and powder produced) obtained at optimum dehulling conditions and predicted values for those characteristics obtained using the models developed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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, 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".