An Integrated Weed Management Strategy for the Control of Canada Fleabane [Conyza canadensis (L.) Cronq.]
Bibliographic record
Abstract
Reports of herbicide-resistant weeds continue to rise at an increasing rate, despite current efforts to mitigate this issue. The repeated use of glyphosate resulted in glyphosate-resistant Canada fleabane [Conyza canadensis (L.) Cronq.] populations in Delaware, USA, in 2001, and Ontario, Canada, in 2010. Research has reported rye (Secale cereale L.) cover crops consistently reduced the density and height of Canada fleabane. This research examined if fall-seeded rye can suppress Canada fleabane seedling density and growth, then the interaction(s) of multiple selection pressures should enhance Canada fleabane control which aids farmers in developing integrated weed management strategies. In 2018 and 2019, field studies were conducted to evaluate the interactions between fall-seeded rye cover crops, shallow fall tillage, and spring-applied herbicides. Interactions between selection pressures were observed to have an additive effect on improving weed control; environmental variables, however, had a direct influence on these interactions.
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 | 0.001 | 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".