Evalutation of Eight Repellents in Deterring Eastern Cottontail Herbivory in Connecticut
Bibliographic record
Abstract
Herbivory by eastern cottontails (Sylvilagus floridanus) can be the source of significant agricultural, nursery, and managed landscape damage. Where cottontails cannot be managed by lethal means or where trap and release is infeasible, repellents may be a reasonable alternative. We tested 8 different repellent formulations (Bobbex Deer Repellent® Canadian formulation concentrate, Bobbex Deer Repellent® Canadian ready-to-use (RTU), Bobbex-R Animal Repellent® concentrate, Bonide Repels All® concentrate, Bonide Deer & Rabbit Repellent® concentrate, Liquid Fence® Deer & Rabbit Repellent concentrate, Plantskydd® soluble powder, and Rabbit Stopper® RTU) on Johnny jump-ups (Viola tricolor), lettuce (Lactuca sativa), and alfalfa (Medicago sativa). Three wild, eastern cottontails were trapped and translocated to a 107 m2 enclosure, resulting in a relative density of 280 cottontails/ha. After 2 weeks exposure to cottontails, remaining plant material was removed, dried, and weighed. Difference between dried plant mass of treated and untreated vegetation was determined. Repellent effectiveness was defined as the sum of the product of caloric demand rank and rank of dry mass difference for each repellent. Physical exclusion performed the best, followed by Plantskydd, Bobbex-R, Bobbex Deer Repellent Canada RTU, Bobbex Deer Repellent Canada Concentrate, Bonide Repels All, Rabbit Stopper, Liquid Fence Deer & Rabbit Repellent, and then Bonide Deer & Rabbit Repellent. Our results show that repellent usage can be a practical solution for deterring rabbit herbivory.
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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".