Evaluating bridge survey ability to detect river otter Lontra canadensis presence: a comparative study
Bibliographic record
Abstract
Many researchers use bridges as search sites to monitor freshwater otter species along watercourses. Bridges enable rapid and easy access to their habitat, but for most otter species little is known on whether these anthropogenic structures affect their distribution, their marking preferences, and consequently, the ability of such surveys to detect their presence. We investigated the bridge survey method using data gathered during four winters of survey along the rivers and streams of Kouchibouguac National Park and surrounding area in New Brunswick, Canada. Our results show that sign surveys using bridges as search sites can have the same capability to detect river otter Lontra canadensis occurrences as surveys using randomly distributed sites. Future surveys can be improved by increasing search distance at bridge sites. This will increase detection rates and safeguard against results underrepresenting otter occurrence in the landscape, which could prompt unnecessary conservation actions. Researchers choosing to increase search distance are advised to augment survey efforts in order to maintain large sample sizes, ensuring sufficient statistical power for tests aiming to detect trends in river otter occurrence.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".