A decision support tool for the first stage of the tempering process of organic wheat grains in a mill
Bibliographic record
Abstract
Summary Wheat tempering conditions grains before a milling process begins. Process adjustments must be made to reach a desired level of flour quality and yield, depending on multiple factors. This article aims to develop a decision support tool to help operators adjust the first‐stage tempering parameters. It is based on a regression model that predicts an increase in organic wheat moisture content according to the properties of the wheat (initial wheat moisture content, wheat protein content and wheat temperature), process parameters (targeted wheat moisture content, wheat flow rate, water flow rate, wheat quantity and resting time) and tempering conditions (water quantity, average day temperature and average day humidity). The increase in wheat moisture achieved during the first tempering stage varies between 0% and 5%. Five regression models were compared: OLS, LASSO, RIDGE, ElasticNet and XGBoost. The models have been developed and tested from a case study at an organic wheat mill. The results indicate that the LASSO model outperformed others, with an average prediction error of 0.428%. The model showed the importance of humidity and temperature factors during the tempering process. The flow of water and wheat were the most influential parameters for an increase in wheat moisture content.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".