Efficacy of CPTH-Treated Egg Baits for Removing Ravens
Bibliographic record
Abstract
Human-altered landscapes have provided resource subsidies for common ravens (Corvus corax) resulting in a substantial increase in raven abundance and distribution throughout the United States and Canada in the past 25 years. Ravens are effective predators of eggs and young of ground-nesting birds. During 2002–2005, we tested whether chicken egg baits treated with CPTH (3-chloro-p-toluidine hydrochloride) could be used to manage raven numbers in an area where raven depredation was impacting sharp-tailed grouse (Tympanuchus phasianellus columbianus) and greater sage-grouse (Centrocercus urophasianus) populations in Nevada. We performed multiple raven surveys at a treatment site and 3 control sites and used videography to identify predators and estimate egg bait consumption. We detected reductions in raven abundances over time at the treatment site during all years of this study and did not detect reductions in raven abundances at control sites. Videographic observations of egg consumption indicated that the standard 1:2 ratio (1 raven removed/2 eggs consumed) substantially overestimated raven take because non-target species (rodents) consumed some egg baits. The technique described here likely will be effective at reducing raven densities where this is the intended management action.
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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 | 0.001 |
| 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 | 0.001 | 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".