Rules-based front-end detector as a bootstrap method for model-based detection of microchiropteran calls
Bibliographic record
Abstract
Rules-based methods for automated acoustical signal processing of bat calls have been developed throughout the history of bat acoustics research, stemming from techniques developed by expert acousticians for hand analysis of bat calls. Recently, a model-based paradigm [Skowronski and Harris, J. Acoust. Soc. Am. 119(3), 1817–1833 (2006)], inspired by automatic speech recognition research, was introduced that improved the accuracy of detection and classification of echolocation calls by an order of magnitude over conventional techniques. However, the models require labeled data for training, and generating call end points by hand is time consuming and labor intensive. As an alternative, a rules-based front-end detector was developed to provide initial call end points for detection models. The combined system allows the models to be trained with unlabeled data, which greatly reduces the amount of time and effort needed to produce accurate detection models. With a sufficient amount of training data from across the spectrum of bat species, the model-based detector is superior to the rules-based detector and also generalizes to calls from species not present in the training data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".