Low-cost automated call box system to conduct playback experiments for wildlife research and management
Bibliographic record
Abstract
Acoustic cues (e.g., vocalizations or 'calls' in wildlife) may effectively change social behavioral patterns in certain species. There are very few published papers on how to create a device at a low cost that can automatically reproduce acoustic cues to carry on wildlife playback experiments or management. We developed the Automated Call Box (ACB) based on an open-source Arduino-compatible microcontroller that attaches to a speaker system and costs less than the most common game caller used by researchers. The unit helps test the influence of social information on habitat choices and has other management applications. We field-tested the ACB to assess the effect of conspecific vocalizations cues on the habitat settlement and to increase the detectability of songbird species. We describe in detail the costs of the materials to build this type of system as a reference for further research. Our ACB allows researchers to conduct experiments on wildlife in inclement weather and complex environments, even in remote locations, with minimum or no necessity for the researcher to be present to reset the equipment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.007 |
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".