Passive Acoustic Monitoring of Gunshot Activity in Cooking Lake-Blackfoot Provincial Recreation Area
Bibliographic record
Abstract
In the past, monitoring hunting behavior has been limited to self-reported numbers. However, the ability of autonomous recording units to monitor soundscapes may make them suitable for assessing spatio-temporal shooting patterns. Our goal for this project was to find out if it is possible to use acoustic monitoring to track human activity, and if there were differences in seasonal or daily shooting intensities. We hypothesized that shooting intensity would decrease from September to November and from the afternoon till morning due to people being less likely to go shooting in cooler temperatures. A grid of 91 ARU’s were deployed between September 2nd and November 30th, 2018 in Cooking Lake-Blackfoot Provincial Recreation Area. They were set to record continuously between sunrise and sunset with some recording during the night as well. We selected a random subset of 30 minute recordings, visualized them using spectrograms; visual representations of sound with time on the x-axis and frequency on the y-axis, and counted the gunshots in each. We compared differences in gunshot detections between months and different times of day using analysis of variance (ANOVA). There were no statistical differences found in seasonal or daily shooting intensities. One reason for this may be that sample sizes were low, due to the time needed to manually process recordings. We demonstrated that ARU’s can be used to provide us with an accurate way of assessing shooting patterns and therefore, be useful for monitoring other human behaviors such as detecting poachers, or assessing compliance with hunting laws.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".