Using noxious weed lists to prioritize targets for developing weed management strategies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it