Acoustic remote sensing of Arctic Sea Ice from long term soundscape measurements
Bibliographic record
Abstract
The Arctic sea ice melting, in the global warming context, has become a major scientific topic during the last 30 years. The Arctic Ocean plays a fundamental role in the global climate balance and requires a particular attention. The Arctic Regions are then monitored by satellite observations and in-situ measurements. The climatic impact of the total melting of the Arctic sea ice is not yet understood and researches are still needed for long term monitoring of Arctic Ocean, particularly the dynamics of the ice cover and its consequences on the ecosystems. The loss of Arctic sea ice will gradually accompanied by the installation of seasonal or perennial industrial activities. As consequences, it will result a modification in the underwater soundscapes in these regions devoid of anthropogenic sound sources. The present study, focused on the Canadian Arctic and subarctic seas natural soundscapes, falls within this context through two research axes. The first part of the present study concerns the direct consequences of the melting of sea ice on Polar Regions soundscapes. We then examined the background noise, its seasonal variations and its environmental drivers. A dedicated algorithm to estimate this ocean noise component has been developed for this purpose, in order to constitute time series from long term acoustic measurements. Through statistical analysis, we determined that the environmental variables responsible for generating the background noise depends upon the state of the ocean surface and that during the winter period, the background noise is controlled by the same environmental variables driving the large-scale Arctic Ocean circulation. The second part of our work is to evaluate the potential of passive acoustics as a complementary means of monitoring the spatial and temporal dynamics of Arctic sea ice. To do this, we identified acoustic events related to the physical phenomena under the ice cover to improve our understanding of their generating mechanisms. We were able to bind various acoustic transients to some deformation processes of the moving ice cover.
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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) | 0.001 | 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 | 0.000 |
| 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".