Don’t believe the mike: behavioural, directional, and environmental impacts on recorded bat echolocation call measures
Bibliographic record
Abstract
Echolocation calls produced by bats in their larynges allow these flying, nocturnal mammals to orient and find food at night. The acoustic signals are not like bird song, and even individual bats exhibit great flexibility in call design and between-species overlap is common. As a result, identifying bats to species by their echolocation calls even in communities with few bat species can be difficult. Unfortunately, the situation is worse still. As a result of several factors — some to do with microphones, some with environment, some with bats, and the calls themselves — acoustic information transmitted to and transduced by microphones can be dramatically different from the actual signal produced by the bat and as would be recorded on axis, close to its mouth using ideal microphones under ideal conditions. We outline some of these pitfalls and discuss ways to make the best of a bad situation. Overall, however, we stress that many of these factors cannot be ignored and do impact our recordings.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".