Correcting Head Rice Yield for Surface Lipid Content (Degree of Milling) Variation
Bibliographic record
Abstract
ABSTRACT Head rice yield (HRY) is the primary parameter used to quantify rice milling quality. However, HRY is affected by the degree of milling (DOM) and thus HRY may not be comparable between different lots if the DOM is different. The objective of this study was to develop a method by which HRY values can be adjusted for varying DOM values when measured by surface lipid content (SLC). Seventeen rough rice lots including long‐grain and medium‐grain cultivars and hybrids were harvested from two 2003 and five 2004 locations. Duplicate subsamples of each lot were milled in a McGill No. 2 laboratory mill for 10, 15, 20, or 40 sec after zero, one, two, three, and six months of storage. HRY and SLC were measured. The average HRY versus SLC slope across all milling duration data sets was 9.4. As such, it is suggested that, when milling with a McGill No. 2 laboratory mill, the HRY of a rice lot can be adjusted by a factor of 9.4 percentage points for every percentage point difference between the rice lot SLC and a specified SLC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".