Efficacy of Biological and Chemical Herbicides on Non-Native Buckthorn during Three Seasonal Periods
Bibliographic record
Abstract
The invasive-exotic Rhamnus cathartica has been growing in parks and natural areas of North America for over 100 years where it has replaced native vegetation. Chemical herbicides have limited success on R. cathartica and often require follow-up applications. This multiyear study is the first to investigate the efficacy of Chondrostereum purpureum, the active agent in Chontrol Peat Paste (CPP), as a biological herbicide for R. cathartica. The objective of this study was to determine the efficacy of CPP and Roundup on R. cathartica trees by comparing re-growth/mortality rates of mechanically wounded trees treated with either herbicide. Rhamnus cathartica trees were girdled or cut and received either CPP or Roundup applications in late-fall (LF), early-summer (ES), and late-summer (LS) at Assiniboine Park in Winnipeg, Canada. All trees were evaluated for mean re-growth, number, and condition of basal sprouts during spring following each application. It was expected that trees treated with CPP would show less re-growth than those that were solely mechanically wounded (controls). In LF, the most effective mechanical/herbicide combination for reducing overall stem re-growth was found to be the cut treatment followed by Roundup. In ES, however, the most effective treatment combination for suppressing re-growth was the CPP application to girdled trees as conditions were optimal for inoculation of trees. These results will allow herbicides to be effectively applied over a longer duration of the season and have implications for the development of future management protocol for R. cathartica in urban parks and natural areas.
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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 | 0.000 | 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".