Ambient noise levels off the coast of Northern \nLabrador
Bibliographic record
Abstract
Ambient sound data was recorded over a 6 month period from October 2017 to March 2018 and a 15 month \nperiod from July 2019 to September 2020. This data was recorded from a mooring at the northeastern \nedge of Saglek Bank off Northern Labrador, where the water depth is 500 meters. High biodiversity and \nlimited shipping activity make this an area of interest. At the mooring location, tidally driven currents are \ndriven to speeds of up to 50 cm/s. The high current speeds result in a significant tilting of the mooring, \nwith the instrument often indicating tilts in excess of 20 degrees. Further, the high speeds lead to mooring \nnoise which corrupts the ambient sound data. We use current meter data to identify periods of reduced \ncurrent speeds where sound data is not corrupted, thereby recovering a record of naturally occurring sounds. \nTidal predictions are used to sort the acoustic data for periods where current speeds are unavailable. The \nreduced data set is used to explore noise levels in this area and obtain surface wind speed and rainfall rate \nestimates. A bottom interaction model is described and evaluated to asses the impact of bottom re \nections \non noise levels and to determine the effect on weather estimates. Within the quiet periods of the sound \nrecordings, whale calls are heard. This thesis also reports on an application of the Phase and Amplitude \nGradient Estimation (PAGE) method in an oceanographic context to estimate acoustic intensity. The PAGE \nmethod employs phase unwrapping such that the vector intensity is estimated past the Nyquist limit, and \nthe direction of the acoustic source is determined with increased accuracy.
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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 | 0.000 |
| 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".