Acoustic Surveys for Bats are Improved by Taking Habitat Type into Account
Bibliographic record
Abstract
ABSTRACT Passive monitoring of bat species via acoustics is a growing field and as a result there are various software programs available that allow for species identification. However, accuracy of these programs is variable and creating a local call library is essential when trying to identify acoustically similar species. In Dinosaur Provincial Park, Alberta, Canada, 3 Myotis species are difficult to distinguish acoustically. We created an echolocation call library from known Myotis evotis, M. lucifugus , and M. ciliolabrum flying in open spaces and near clutter to test whether or not recording in the bats' local habitat type improved call library quality and identification accuracy. Bat calls recorded within open spaces (coulees) differed from those recorded near cluttered spaces (tree edges) for M. ciliolabrum and M. lucifugus . Accuracy of species identification also increased when we used models based on bat calls recorded in cluttered habitats. Using a simple model with recordings from different habitat types within a study site, we were able to improve identification accuracy and model performance. © 2019 The Wildlife Society.
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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 | 0.001 | 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) | 0.001 | 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".