Spatial patterns of anticoagulant rodenticides in three species of medium-sized carnivorans in Pennsylvania
Bibliographic record
Abstract
Human influences on natural environments are now ubiquitous but manifest in multiple and unique ways depending on local environments and communities. Attempts to control, or mediate, local pests to residences or to agriculture can impart important negative consequences on systems. Secondary exposure to anticoagulant rodenticides (ARs) can cause numerous adverse effects on wild carnivores including death. Few studies have quantified AR prevalence, investigated their pathway of exposure, or associations with specific location types in the northeastern U.S. We hypothesized that ARs would be found in the mesocarnivore community throughout Pennsylvania and have the greatest detection rate in highly urbanized or agricultural landscapes. From 2019 through early 2022, we collected carcasses to obtain liver samples ( n = 265) from three species of carnivores: bobcats ( Lynx rufus (Schreber, 1777)), fishers ( Pekania pennanti (Erxleben, 1777)), and river otters ( Lontra canadensis (Schreber, 1777)). We used generalized linear models to test for differences in AR detection rates among species and spatial scales including the six Pennsylvania Game Commission regions and 23 wildlife management units. We detected ARs in all species (44.2% collectively), but detection rates differed among species. Our study is the first to document ARs within North American river otters.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".