Acoustic Approaches to Locating Bat Roosts and Identifying Drivers of Bat Activity in Canada’s Largest Conurbation
Bibliographic record
Abstract
Acoustic survey methods are used to track bat populations declines and to address ecological knowledge gaps. I tested two applications of acoustic monitoring in Toronto’s Rouge National Urban Park. First, to identify whether acoustic data can locate bat maternity roosts, I tested whether Eptesicus fuscus activity detected by acoustic monitors is predicted by a monitor’s distance to known roosts, and time elapsed since sunset. Time and distance predicted bat activity, but effect sizes were small, limiting the precise location of roosts based on acoustic data. Second, I tested how the amount of available bat habitat affects bat activity, using 7 years of acoustic data. Most landscape features I tested did not predict bat activity, although activity of northern myotis (Myotis septentrionalis) was positively associated with forest cover. Temperatures above 10°C increased bat activity, while precipitation reduced activity. This thesis highlights both the strengths and limitations of bat acoustic surveys.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".