Arctic ambient noise measurements in support of the Northern Watch Project
Bibliographic record
Abstract
During August 2012, acoustic recording systems were deployed in Barrow Strait as part of the Defence Research and Development Canada (DRDC) Northern Watch Technology Demonstration Project. Two Starfish Sensor Cubes each with a 1-m cube of seven hydrophones operating in the frequency range of 5–750 Hz, and two single-hydrophone, Autonomous Multichannel Acoustic Recorders (AMAR) providing a 30-kHz signal bandwidth were deployed. The Starfish were deployed for two one-week intervals. One AMAR was deployed for two weeks partially overlapping the Starfish deployment. The second AMAR was deployed for a period of one year with recovery planned for August 2013. The observed underwater noise picture is one of high variability ranging from an extremely quiet to a noisy environment. Noise sources included: A 500-m long iceberg grounded within 500 m of one of the Starfish; a large ice island (4–5 sq-km) that passed within 4 km of the sensors; a small number of motoring vessels; significant wind events that caused rapid and strong variations in the noise field; and a small number of marine mammal detections. After our departure, a large number of Beluga whales were observed visually. The remaining AMAR may detect these late summer visitors.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".