Using physical oceanography to improve transmission loss calculations in undersampled environments
Bibliographic record
Abstract
The vertical sound speed profile (SSP) is a critical input to any acoustic propagation model. However, even when measured SSPs are available they are frequently noisy “snapshots” of the SSP at a single moment in time and space and do not fully capture changes such as solar heating and wind-driven mixing that can significantly affect shallow water propagation on time scales of less than a day. Furthermore, SSPs measured in the field may not extend to the ocean bottom and are often based on measured profiles of temperature with an implicit assumption of constant salinity. In April–May 2013, the Target and Reverberation Experiment (TREX) was conducted in the Northeastern Gulf of Mexico near Panama City, Florida, a region strongly affected by local wind forcing, freshwater inputs, and the presence of a warm-core Gulf of Mexico Loop Current eddy ("Eddy Kraken") offshore of the experimental site. “Synthetic” SSPs were constructed for the trial area by combining knowledge of the physical oceanography and water masses in the area with the measured SSPs that were available. Transmission loss was modelled using both synthetic and measured SSPs and the results will be compared with measured transmission loss.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".