Bibliographic record
Abstract
ABSTRACT The impact of a smaller sample of whole meal wheat (5.5 or 6.0 g) to replace the official 7.0 g used in the Falling Number test was investigated using samples of Canada Western Amber Durum (CWAD) and Canada Western Red Spring (CWRS) wheat. Use of either of the smaller sample sizes resulted in a significant shortening of the analysis time of the test. Reproducibility studies, using high and low falling number (FN) CWRS and CWAD samples, with three analyses per day over six days indicated no appreciable change in the coefficient of variation for the test using a smaller sample size. The maximum daily standard deviation of three replicates, 25.7 sec, was observed using the official 7.0‐g moisture‐corrected CWAD sample and was not significantly different from the 23.0 sec value obtained for a 5.5‐g sample of CWRS. The maximum average standard deviations observed over the six days of analysis were 13.7, 7.7, and 5.3 sec for the 5.5‐, 6.0‐, and 7.0‐g sample sizes, respectively, and were all associated with the sound, high FN, CWRS sample. While the use of 5.5 g allowed significant differentiation between high and low FN CWRS samples, the ability to discriminate high and low FN CWAD samples was lost at this sample size. FN analysis of CWRS (n = 144) and CWAD (n = 141) at 7.0 vs. 6.0 g, yielded correlation coefficients of 0.95 and 0.88, respectively. Regression analysis indicated a ±14.1 sec error associated with estimating a 7.0‐g FN value using 6.0 g of CWRS which increased to 26.4 sec for CWAD. Particle size analyses of whole meal after grinding indicated that the harder CWAD wheat fractured into a significantly greater percentage of larger particles than the corresponding CWRS. This difference may be a contributing factor to the greater variance associated with the CWAD 6.0‐g test and the inability to differentiate sound and slightly sprouted wheat at 5.5 g. Use of the 6.0‐g method, stopping the test after 200 sec, would also be applicable for screening purposes at commercial facilities where normal testing times (350–450 sec) could be reduced to meet designated quality requirements. This would offer a savings of 2.5 min/sample over the conventional method.
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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 | 0.014 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".