Observed transmissions and ocean-ice-acoustic coupled modelling in the Beaufort Sea
Bibliographic record
Abstract
An ocean-ice-acoustic coupled model is configured for the Beaufort Sea. The model uses outputs from a data assimilating global scale ice-ocean-atmosphere forecast to drive a bimodal roughness algorithm for generating a realistic ice canopy. The resulting range-dependent ice cover obeys observed roughness, keel number density, depth, and slope, and floe size statistics. The ice is inserted into a parabolic equation acoustic propagation model as a near-zero impedance fluid layer along with a model defined range-dependent sound speed profile. Year-long observations of transmissions at 35 Hz from the Coordinated Arctic Acoustic Thermometry Experiment and 925 Hz from the Arctic Mobile Observing System source were recorded over the winter of 2019-2020 on a free-drifting, eight-element vertical line array designed to vertically span the Beaufort duct. The ocean-ice-acoustic coupled model predicts receive levels that reasonably agree with the measurements over propagation ranges of 30-800 km. At 925 Hz, seasonal and sub-seasonal ocean and ice driven variations of propagation loss are captured in the data and reproduced in the model.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".