Investigating the performance of soundscape metrics using known data sources and numerical simulations
Bibliographic record
Abstract
Metrics such as the acoustic complexity index, acoustic diversity index, entropy, evenness, and roughness have been correlated with anthropogenic and biologic sound sources in terrestrial soundscapes. The metrics offer the possibility of characterizing the presence of sound sources and the biodiversity of an environment in large datasets without having to perform detailed automated or manual analysis. However, the metrics are less successful at separating soundscape components and measuring biodiversity when applied to marine soundscapes. The reasons provided for the are the spectral overlap between anthropogenic and biologic sources, the wide range of source levels, long propagation ranges, and the difficulty comparing acoustics to observations, especially for species that are soniferous at night and hidden by day. In this analysis, we examine several popular soundscape metrics for marine data containing typical ambient noise, anthropogenic sources, or a single species. We then examine how the metrics change as different magnitudes and repetition rates of simulated marine life vocalizations are added to the data files.
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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 | 0.010 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".