Canada Thistle (Cirsium arvense) Response to Clipping and Seeding of Competitive Grasses
Bibliographic record
Abstract
Chemical restrictions, ecological concerns, liability issues, and public sentiment present challenges to land managers attempting to control highly invasive plants like Canada thistle (Cirsium arvense [L.] Scop.). Although herbicide application can be an effective control strategy, increasing limitations force managers of sensitive environments (e.g., national parks, wildlife refuges, protected water-bodies or waterways) to search for effective control alternatives. A greenhouse study was conducted to test the effectiveness of clipping (to simulate field mowing) and grass seeding as alternatives for Canada thistle control. Two native North American grasses (western wheatgrass [Pascopyrum smithii {Rydb.} A. L?ve] and streambank wheatgrass [Elymus lanceolatus {Scribn. & J.G. Sm.}Gould ssp. lanceolatus]) and one sterile hybrid cross between common wheat (Triticum aestivum L.) and tall wheatgrass (Thinopyrum ponticum [Podp.] Z.W. Liu & R.C. Wang) called RegreenTM were used. The effects of clipping and grass seeding on Canada thistle growth, and the effect of Canada thistle on grass growth, were evaluated using 14 unique treatments applied to potted Canada thistle and grass plants. Clipping inhibited Canada thistle growth (by 60%), while grass seeding had no effect. Presence of Canada thistle inhibited grass growth for all seeding treatments except when RegreenTM and western wheatgrass were seeded together with Canada thistle. Planting multiple species for restoration of Canada thistle-infested sites may be important (RegreenTM + western wheatgrass treatment), and cutting Canada thistle may be useful for reducing its growth in restored areas.
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 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".