An assessment of the efficacy of rub stations for detection and abundance surveys of Canada lynx (<i>Lynx</i> <i>canadensis</i>)
Bibliographic record
Abstract
Barbed and scented rub pads that rely on a cheek-rubbing behavioural response are a standard survey design that has been used extensively across the range of Canada lynx (Lynx canadensis Kerr, 1792). However, there have not been any published studies evaluating the effectiveness of rub stations for detecting lynx by comparing other simultaneous survey methods. We used a combination of paired rub stations and remote cameras at 41 sites to compare detection probabilities between the two methods and conduct a mark–recapture population estimate of Canada lynx using rub stations to further interpret our findings. The detection probability calculated using cameras approached 1.0 for most of the winter season (mean = 0.88), whereas it remained less than 0.52 for hair rub stations (mean = 0.27). The low and variable detection probability using hair snags, high detection probability using cameras, and the potential gender or individual bias in rubbing behaviour based on our mark–recapture analysis suggest that rub stations are not the most efficient survey method available for Canada lynx. Until additional research incorporating spatial scale, seasonal timing, gender bias, and survey design is conducted, we urge caution in the use of hair stations that rely on the cheek-rubbing behaviour of Canada lynx.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".