Classical Weed Biological Control Outcomes: A Catalogue-based Analysis of Success Rates and Their Correlates
Bibliographic record
Abstract
Classical weed biological control (hereafter CWBC) is an important and effective management tool that may see an expanded role as more problematic weeds emerge worldwide, more countries begin to adopt this approach, and broader applications of the practice are explored. However, success is never certain in CWBC, and predicting success remains an elusive goal. Here, a series of catalogue-based analyses of CWBC outcomes are conducted in order to quantify success rates and to explore potential factors that may be associated with success. Statistical analyses utilizing chi-squared tests were utilized to search for correlations between CWBC efficacy and various host and agent characteristics. Multiple such relationships were identified, including previously identified correlations between CWBC outcomes, host habitat types, and agent orders. Significant differences in CWBC outcomes are also correlated to agent feeding guilds, which represents a novel association revealed by this study that warrants further investigation. For example, defoliating agents were associated with lower establishment rates, and root boring agents were associated with higher control efficacy. Success rates derived from the most recent catalogue were also delineated by region and time period, and management implications are discussed.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".