Bibliographic record
Abstract
The objective of this thesis is to determine the frequency and wind-wave forcing dependent effective sea surface noise source level per unit area extracted from the hourly minimum sound power levels of six month-long acoustic recordings. The effect of the propagation environment is accounted for using Bellhop. The simulated environment is configured using climatological sound velocity profiles to capture seasonal effects and bottom sound speed estimates made from seabed sediment maps. Hourly meteorological data were extracted from ERA5 providing relevant wind and wave parameters from which noise levels may be predicted. A weighted composite model consisting of neutral wind and significant wave height leveraging the two-term exponential regression function proved to maximize model R2. Received level data originating from 16 hydrophone stations in the North Atlantic and Labrador Sea were combined with the Bellhop TL simulations in order to produce estimates of the effective noise source level per unit area (NSL/A) for changing surface environmental conditions and inter-compared. Hourly minimum sound power level derived model-data comparisons using horizontal wind speed magnitude 10 m above sea level expressed a decrease in NSL/A estimates versus Kewley (1990) by 10 to 15 dB from 1 to 3 kHz.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".