Bat Noise Scrubber Bias Adjustment Using the Rogan-Gladen Estimator and Bayesian Inference
Bibliographic record
Abstract
The number of bat call files recorded in acoustic surveys may be used to assess comparative bat activity levels over time and among habitats. Acoustic signal processing can segregate bat files from noise files and thus quickly provide an estimate of the number of bat files in a large sample of recordings. However, false positive and false negative classifications may result in a biased estimate requiring adjustment, as inaccurate bat numbers may impact bat conservation decisions. Previous research has ranked software classification accuracy in comparison to the visual classification of spectrograms. Small classification errors can result in considerable bias in software-derived estimates of the number of bat call files in a sample. Estimation bias may not have a linear relationship to the percentage of files containing bats, requiring unique correction coefficients. The focus of this note is to 1) illustrate patterns of bias that may result from noise scrubbing and 2) to illustrate the application of two methods of bias adjustment, the Rogan-Gladen estimator and Bayesian inference. The expected bias of four noise scrubbing tools from the literature, each of different measured accuracy, was plotted over a simulated range of true bat file prevalence while holding constant the accuracy of each scrubber. Rogan-Gladen bias adjustment was accurate for all four noise scrubbers. Bayesian bias adjustment showed low overall error, with some inflation at very low bat file prevalence. Caveats in the use of both bias adjustment methods are discussed.
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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 | 0.028 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".