Arctic underwater noise transients from sea ice deformation: Characteristics, annual time series, and forcing in Beaufort Sea
Bibliographic record
Abstract
A 13-month time series of Arctic Ocean noise from the marginal ice zone of the Eastern Beaufort Sea is analyzed to detect under-ice acoustic transients isolated from ambient noise with a dedicated algorithm. Noise transients due to ice cracking, fracturing, shearing, and ridging are sorted out into three categories: broadband impulses, frequency modulated (FM) tones, and high-frequency broadband noise. Their temporal and acoustic characteristics over the 8-month ice covered period, from November 2005 to mid-June 2006, are presented and their generation mechanisms are discussed. Correlations analyses showed that the occurrence of these ice transients responded to large-scale ice motion and deformation rates forced by meteorological events, often leading to opening of large-scale leads at main discontinuities in the ice cover. Such a sequence, resulting in the opening of a large lead, hundreds by tens of kilometers in size, along the margin of landfast ice and multiyear ice plume in the Beaufort-Chukchi seas is detailed. These ice transients largely contribute to the soundscape properties of the Arctic Ocean, for both its ambient and total noise components. Some FM tonal transients can be confounded with marine mammal songs, especially when they are repeated, with periods similar to wind generated waves.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".