Bibliographic record
Abstract
Afzalinia, S., Shaker, M. and Zare, E. 2004. Comparison of different rice milling methods. Canadian Biosystems Engineering/Le genie des biosystemes au Canada 46: 3.633.66. This study was conducted to determine the best rice milling method in the Fars province of Iran. First, the effect of the paddy moisture content on the milled rice breakage was evaluated. Then four different milling systems were compared and, finally, an economic evaluation was performed to justify the economic performance of the selected method. Results of this study showed that the optimum paddy moisture content for the milling process was 12 to 14% wet basis (wb), and using three abrasive whiteners in series and one friction whitener as a polisher resulted in the least rice breakage, proving this method to be the best choice for the rice milling operation in the province of Fars. Economic evaluation confirmed the results of the method of comparison. Le but de cette etude etait de determiner la meilleure methode pour le decorticage du riz dans la province de Fars en Iran. L’effet de la teneur en eau des grains de riz non decortiques sur le bris des grains de riz a d’abord ete evalue. Ensuite, quatre systemes de decorticage differents ont ete compares et finalement, une evaluation economique a ete faite pour justifier les performances economiques de la methode selectionnee. Les resultats de cette etude demontrent que la teneur en eau optimale des grains non decortiques pour le procede de decorticage etait de 12 a 14% (base humide) et que l’utilisation en serie de trois blanchissants abrasifs et d’un blanchissant de friction pour le polissage final provoquait le moins de brisure des grains, prouvant ainsi que cette methode constitue le meilleur choix pour les operations de decorticage du riz dans la province de Fars. L’evaluation economique a confirme les avantages de cette technique.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".