Using autonomous recording units for vocal individuality: insights from Barred Owl identification
Bibliographic record
Abstract
Recent advances in acoustic recording equipment enable autonomous monitoring with extended spatial and temporal scales, which may allow for the censusing of species with individually distinct vocalizations, such as owls. We assessed the potential for identifying individual Barred Owls (<em>Strix varia</em>) through detections of their vocalizations using passive acoustic monitoring. We placed autonomous recording units throughout the John Prince Research Forest (54°27' N, 124°10' W, 700 m ASL) and surrounding area, in northern British Columbia, Canada, from February to April 2021. The study area was 357 km<sup>2</sup> with a minimum of 2 km between the 66 recording stations. During this period, we collected 454 Barred Owl calls, specifically the <em>two-phrase hoot</em>, from 10 recording stations, which were of sufficient quality for spectrographic analysis. From each call, we measured 30 features: 12 temporal and 18 frequency features. Using forward stepwise discriminant function analysis, the model correctly categorized 83.2% of the calls to their true recording location based on a 5-fold cross validation. The model showed substantial agreement between the recording station that the call was classified to originate from, and where the call was actually recorded. The most important features of the calls that enabled discrimination were call length, interval between the 4th and the 5th note, interval between the 6th and 7th note, and duration of the 8th note. Our results suggest that passive acoustic monitoring can be used not only to detect presence/absence of species but also, where vocalizations have individually distinct features, for population censusing.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".