Field tests of small autonomous recording units: an evaluation of in-person versus automated point counts and a comparison of recording quality
Bibliographic record
Abstract
The proliferation of small autonomous recorders makes it easier than ever to sample terrestrial acoustic animals and soundscapes. I conducted a comparison of four small recorders to evaluate their performance in a field setting: Wildlife Acoustics Song Meter Mini; Wildlife Acoustics Song Meter Micro; Open Acoustics Audiomoth; and Cornell SwiftOne. I address two questions: (1) How do in-person point counts compare to recorder-based point counts using these small autonomous recorders? (2) How does the quality of the recordings compare across these small autonomous recorders? To evaluate the performance of the recorders in point counts, I conducted in-person and recording-based point counts at ten locations. Each of the recorders performed similarly well at point counts, producing comparable estimates of species richness, although all of the autonomous recorders under-estimated species richness. To evaluate recording quality, I conducted a sound transmission test, broadcasting and re-recording sounds. Recorders varied in their frequency response above 12 kHz, but showed only subtle differences in the frequency response at frequencies below 12 kHz. I conclude that each of these types of small recorders provide bioacousticians with useful tools for conducting point counts, and for passive monitoring of animal sounds, with only subtle differences across the investigated models.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".