Comparison of observed and predicted reflection loss in the Beaufort Sea using a multilayer, rough, 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 new 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 10–20 degrees that reflect off the ice 3–10 times. Worcester et al. (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. We have developed a four-layer (water, skeletal ice, solid ice, and air), acousto-elastic reflection loss model that incorporates roughness using the Rayleigh formula. When combined with in-situ observations of ice thickness and roughness, as well as physical parameters for ice from literature, the model shows good agreement with the observations during the maximum ice thickness period in June. Comparisons will be made for the ice growth and melting phases, sensitivity to ice parameters will be investigated, and the results will be interpreted in terms of first year ice rheology.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".