Factors impacting the detection of weed seed contaminants in seed lots
Bibliographic record
Abstract
BACKGROUND: The setting and following of phytosanitary standards for weed seeds can lessen the impacts of weeds on agriculture. Standards adopted by seed companies, laboratories and regulators ensure the contamination rates do not exceed some thresholds. Globally sample size standards are set based on the amount needed to obtain a contaminant in a random sample of the seed lot, not detectability. New Zealand requires a 95% confidence that the maximum pest limit of 0.01% of quarantine weed seed contamination is not exceeded in an imported seed lot. We examined 24 samples each containing approximately 150 000 seeds of either perennial ryegrass (12 samples) or white clover seeds (12 samples) that were then spiked with seeds (contaminants) from 12 non-crop species (3-8 seeds of each). We considered factors that may impact detection rates: shape, color, size, and texture relative to the crop, and technician (including a commercial seed laboratory). RESULTS: A linear mixed model fitted to the data indicated significant observer, crop, and seed color, shape, and size effects on detection. Detectability increased by 20% ± 7.7 (± standard error) when seeds had a distinct shape or color (28% ± 8.1), or were larger (23% ± 8.7) rather than smaller, relative to the crop. Commercial laboratory identifications were usually correct at the level of genus, and species for common weeds, but some misidentifications occurred. CONCLUSION: Sample sizes for border inspections should be based on detectability of regulated weed seeds in the crop in combination with weed risk for the crop and location. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".