Cryoacoustic inversion for Beaufort Sea ice properties during 2016–2017 using a rough, layered acousto-elastic reflection coefficient model
Bibliographic record
Abstract
The Canada Basin Acoustic Propagation Experiment (CANAPE) conducted during 2016–2017 utilized a 150-km radius, seven-mooring acoustic tomography array to examine acoustic propagation in the changing Arctic. Broadband acoustic transmissions with center frequencies of 172.5, 250–255, and 275 Hz revealed identifiable and trackable ray-like arrivals with grazing angles of 11°–19° that reflect off the ice 3–10 times. Worcester etal. (2024) [J. Acoust. Soc. Am. 156, 4181–4192] showed that the maximum excess transmission loss per surface reflection, defined as the increase in transmission loss relative to open water conditions, varies from 2–6 dB and is strongly frequency and angle dependent. The loss scales roughly with ice thickness. A four-layer (water, skeletal ice, solid ice, air), acousto-elastic, plane-wave reflection loss model that yields loss predictions in line with the results of Worcester et al. (2024) using the observed ice draft and ice parameters from the literature is used here in a cryoacoustic, Bayesian inversion to predict the time evolving ice properties over an annual cycle. The model incorporates roughness using the Rayleigh formula. Challenges include nonlinearity, missing physics, and questions of how well the data constrains 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".