How green is green? Sampling and perception in assessing green seeds and chlorophyll in canola
Bibliographic record
Abstract
Abstract Green seeds are used as a grading factor in estimating chlorophyll in canola and rapeseed in the Canadian and U.S.A. grading systems. This work examines the effect that sampling and perception have on the estimation of green seeds as well as the effect that sampling has on the determination of chlorophyll. Individual seed analysis indicated that in order to be considered as green, seeds needed to contain between 200 and 400 mg/kg chlorophyll. Variation due to binomial sampling played a predominant role in the error in determining the green seed levels in canola. Sampling of large numbers of seeds, as in the loading of export shipments, reduced the error. Binomial sampling also contributed to the error in chlorophyll determination even with sample sizes as large as 500. Differences in perception of green also were noted between individuals with coefficients of variation as high as 50% at the 1% green seed level. The combination of perception error and sampling error may result in samples of 1,000 seeds drawn from a mass with 2% green seeds having green seed counts ranging from 0.96 to 3.04%, 19 times out of 20.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".