Lack of Response to Olfactory Lures Among Mammals in Riparian Habitat in Southern Wisconsin
Bibliographic record
Abstract
Non-invasive mammal surveys often employ olfactory stimuli on the assumption that they will attract mammals and increase the success of monitoring projects. However, information on the effectiveness of scent lures is variable and often relies on data generated from mammal tracks or sign, which can be challenging to quantify. Therefore, we sought to determine whether certain olfactory stimuli are more effective than others at eliciting a response from mammals along riparian corridors in Southern Wisconsin, using camera traps to monitor response to four scents and a control of de-ionized water. We recorded the number of times each species responded as well as the length of time spent investigating scents (Muskrat gland, mink gland, Red fox urine, crayfish oil) and the control. We recorded 2812 passes by 16 mammal species during our 12-month study, with Virginia Opossum (Didelphis virginiana), Woodchuck (Marmota monax), Eastern Gray Squirrel (Sciurus carolinensis), Eastern Fox Squirrel (Sciurus niger), Raccoon (Procyon lotor), Coyote (Canis latrans), and White-tailed Deer (Odocoileus virginianus)observed on cameras most frequently. However, 72% of observations involved mammals passing through without investigating any scents or the control, and among the 28% of observations where mammals did investigate, we found no significant differences in their response to specific scents or the control or in response by season. Further analysis revealed no significant differences in the time mammals spent investigating individual scents or the control. The lack of response suggests that factors other than scent may have attracted wildlife to our stations. Although under some circumstances olfactory attractants may increase the level of response to monitoring stations, we suggest that attraction to our stations was largely a behavioural response to novel stimuli in the environment.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".