How high is high? Using percentile thresholds to identify peak bat activity
Bibliographic record
Abstract
Passive acoustic monitoring is a common tool used for monitoring bat activity levels. Identifying periods and locations of peak levels provides insight into bat ecology and has important management implications. One limitation of passive acoustic monitoring is the relative nature of the data, often relying on subjective interpretation of descriptive terminology such as “higher” or “lower”. We propose the use of percentile thresholds (PTs) for objectively identifying peak activity. By compiling a reference data set, it is possible to define percentiles of the observed activity levels and these percentiles can provide objective thresholds for comparing activity levels. We used acoustic recordings from sites in Canada and calculated PTs based on the distribution of the number of calls per hour across all nights and sites for three species of bat. Given species ecologies (e.g., hibernating, migrating), we were able to use PTs to objectively identify peak activity levels on a species-specific basis. Percentile thresholds are also a replicable method of describing within-night activity by evaluating species-specific activity patterns and important times of night. Our analyses and examples represent a proof of concept. The next step is to move towards a standardized distribution to generate PTs. Creating a public repository of acoustic data sets to evaluate activity of a species in the context of its entire range would allow us to standardize terms such as “high” activity in an objective manner.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".