Going Beyond a Leap of Faith When Choosing between Active and Passive Bat Monitoring Methods
Bibliographic record
Abstract
The limiting trade-off between expediency and accuracy is well exemplified by the monitoring of bats, more specifically since the onset of the White-Nose Syndrome in North America. Acoustic detection is a way of circumventing the difficulties of catching bats, and monitoring is usually done either with transects or fixed recording points, the latter generally being assumed to be superior. However, little has been done until now to assess each method's ability to maximize the quality of recordings and the number of species detected, to account for the temporal variability of bat activity, and to account for the variability in habitats and the spatial patterns of bat activity. We tested whether transects could yield similar results as fixed points for every of those aspects of accurate and reliable bat monitoring. We found it to be true for recording quality and the detection of peaks of activity, but found that either method was little affected by weather and landscape attributes. We conclude that the use of transects is a valid choice for long-term monitoring, as it performs comparably to fixed recording points and maximizes the number of detections per sampling unit. However, transects tend to record a greater proportion of migratory bats than fixed recording points, a bias which should be considered in the assessment of the state of particular species' populations.
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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 | 0.185 | 0.230 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".