Soundscape characterization in a dynamic acoustic environment: Grand Passage, Nova Scotia, a planned in-stream tidal energy site
Bibliographic record
Abstract
The marine environment in the Bay of Fundy hosts a dynamic and diverse soundscape that is a fundamental component of the local ecosystem. The emergence of new human marine activities and infrastructure, such as tidal turbine installations, introduces new sound sources that change or disrupt the existing acoustic environment, but the full extent of these changes is not well understood and is not predictable. To better evaluate the effects of future tidal energy development on the local soundscape in Grand Passage, Nova Scotia, a thorough understanding of the pre-development characteristics must be established. The present research seeks to identify existing anthropophony, biophony, and geophony, and to evaluate the underlying mechanisms contributing to geophonic variability. Passive acoustic measurements have been conducted using long-term moored omnidirectional hydrophones, a moored 5-channel array, and drifting hydrophone arrays, enabling identification of dominant signals and source direction, estimation of pseudonoise masking effects due to turbulent flow, and analysis of diurnal, daily, seasonal, and annual variability as well as spatial variability within the study area. This paper provides an overview of the dominant sound sources and discusses the effects of environmental forcing on soundscape characteristics.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".