Bibliographic record
Abstract
A four-element three-dimensional hydrophone array has been deployed on the Strait of Georgia node of the Victoria Experimental Network Under the Sea (VENUS) since March 2020. To the present, continuous pressure time series data from the four sensors has been streamed ashore and made available by Ocean Networks Canada. A technique for classifying and partitioning ambient noise using the three-dimensional noise coherence function is applied to the recordings. The noise coherence (directionality) due to wind generated surface noise and individual ships is analytically modelled using measured environmental inputs such as the time varying sound speed profile and sediment properties from the measurement site and compared with the observation. The theoretical coherence curve is computed by mixing the contributing sources until a best-fit is found. Since the wind generated surface noise coherence is stable and independent of the source effective sound power per unit area, the contribution of ship noise to the soundscape can be exactly determined. Applying this algorithm to the long-term data set allows the relative contribution of ship noise to the soundscape in the region to summarized succinctly. [Research supported by ONR.]
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".