Pathways of Invasive Plant Spread to Alaska: III. Contaminants in Crop and Grass Seed
Bibliographic record
Abstract
Abstract Invasive plants disperse to new areas via numerous pathways. Study of these pathways helps to focus limited budgets toward prevention and early detection. This study examined potentially invasive seed contaminants in imported crops and grass seed as pathways for plant dispersal to Alaska. Crop and grass seed were purchased from 13 Alaska retail outlets representing 14 seed suppliers. Seed bags were sampled using federally mandated protocols and were analyzed for crop seeds that were not supposed to be included and for weed contaminants. Ninety-five weed and 36 contaminant crop taxa were found. Crop seed contained 43 weed taxa and 15 other crop species contaminants, a mean of 6.4 taxa and 3,844 contaminant seed kg−1. Grass seed samples contained 73 weed taxa and 21 crop contaminants, a mean of 3.5 contaminant species and 1,250 seeds kg−1. Two species prohibited by the Alaska seed law were found: Canada thistle was found in a single crop sample, and quackgrass was found in two grass samples. There were no significant relationships between either seed type or supplier and either the number of contaminant species or number of seeds. Labels of 33% of crop samples and 8% of grass samples claimed 0.00% weed seeds, but low (0.007% by weight, 2 species) to high (1.18% by weight, 13 species) amounts of weed contaminants were found. Importation of crop seed is a large pathway for seed movement, causing significant propagule pressure and an increased likelihood of establishment by new invasive plant populations. Prevention of spread via this pathway would be enhanced by changes to seed laws, by greater regulatory enforcement, and by including on the label, the names of all weed and contaminant crop species found in the law-required samples. Consumers could then make decisions on whether to purchase seed based on the potentially invasive species that would be planted with the desired seed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".