Listen But Do Not Touch: Using a Smartphone Acoustic Device to Investigate Bat Activity, with Implications for Community-Based Monitoring
Bibliographic record
Abstract
The monitoring of bats across the world is mostly conducted using invasive mist-netting, whereby vertical nets are placed to capture bats mid-flight. Many studies have demonstrated how this approach causes sampling bias, is labor-intensive and increases the risk of white-nose syndrome fungus, Pseudogymnoascus destructans, transmission among bats. Increasingly, acoustic devices are being employed to collect data on bat activity and richness. Community-based monitoring is an important data collection source for bat monitoring programs in countries such as the UK (National Bat Monitoring Program), whereby walking bat transects are conducted using bat detectors. Since the launch of smartphone devices to record and auto-identify bat echolocation calls, the quality of data collection that community members can collect has increased significantly, however, this approach is seldom used to generate data in scientific studies. In our study, we have showcased how our study design paired with state of the art acoustic monitoring devices, can be applied to community-based monitoring of bats across the world. Through employing smartphone acoustic devices, we have determined how primary and secondary vegetation cover are predictors of bat species occurrence and identified the importance of riverine and deciduous swamp habitats for rare bat species in southwestern Ontario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".