Using noxious weed lists to prioritize targets for developing weed management strategies
Bibliographic record
Abstract
To identify the most commonly regulated weedy plants in the United States and southern Canada, we compiled a database of noxious weed lists obtained from the 48 continental states and six bordering provinces. The 10 most frequently listed weeds are Cirsium arvense, Carduus nutans, Lythrum spp. (includes purple loosestrife), Convolvulus arvensis, Euphorbia esula, Acroptilon repens, Sorghum spp. (includes johnsongrass and shattercane), Cardaria spp. (includes hoary cress, also called whitetop), Centaurea maculosa, and Sonchus arvensis. When genera are ranked, the top genus is Centaurea, which includes C. maculosa, C. diffusa, and C. solstitalis. Biological control programs have targeted many of the top dicotyledonous weeds of national concern, but none of the weedy grasses and sedges. We recommend that exploratory studies be initiated to determine the feasibility of developing biological control agents for the latter species. The complete database of noxious weed lists is available on the Internet at http://invader.dbs.umt.edu. This information may be useful to resource managers and regulatory officials in assessing which weeds are problematic in adjacent geographic areas and by researchers to help select which weeds to target with new management strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".