Navigating the bioacoustic landscape: Standardization and interoperability of acoustic data products
Bibliographic record
Abstract
The importance of bioacoustics for understanding and protecting the natural world has grown significantly over the last few decades. Our capacity to monitor the acoustical landscape has increased with lower cost instrumentation, improved communication, infrastructure, and storage, as well as the development of reliable machine learning methods for analysis. Understanding long-term trends in animal populations as well as the impacts of anthropogenic noise requires the ability to retain and interpret records over decadal time scales, record in detail what type of analysis has been performed and understand when multiple analyses may be combined and when they should not be. This is not possible without a well-defined set of terms and meanings. In this talk, we will provide an overview of the work of Acoustical Society of America’s working group S3-SC1-WG7 that has been charged with developing an American National Standards Institute standard for bioacoustics information derived from acoustic recordings. The proposed standard details what information should be recorded for bioacoustics recording and analysis effort, ranging from specification of data to be noted about instrumentation to information gleaned from recordings such as noise levels, animal calls, and acoustic source locations.
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 | 0.093 | 0.103 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.014 | 0.025 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".