A few Canadian contributions to underwater acoustics
Bibliographic record
Abstract
An historical perspective on four Canadian achievements in underwater acoustics will be presented: Towed Variable Depth Sonar (TVDS); Chapman-Harris model for reverberation; seabed-interaction in shallow water, and uninhabited underwater vehicles (UUVs) for Arctic acoustics. The poor performance of hull-mounted sonars in Canadian waters during WWII prompted TVDS development in 1947. The resulting prototype (CAST/1X) was tested against UK and US developmental systems in 1958. Subsequently, the UK purchased the Canadian system and the US “re-packaged” their sonars according to the Canadian design. An understanding of the effects of back-scattering from the sea surface and bottom was required to model sonar system performance. Experiments began circa 1960 leading to a new model for sea surface back-scattering, and revealing the ocean volume as an additional factor. The conditions governing propagation and noise in shallow waters remained mysterious in the 1970s, until the significant role of the seabed emerged through theoretical developments (e.g., loss due to shear-waves) and experiments conducted over a variety of seabed types. Political, sovereignty and energy issues prompted Canada to investigate under-ice acoustics in the Arctic beginning circa 1957. The efforts ultimately resulted in the ability to undertake under-ice acoustic and geophysical surveys using long endurance UUVs.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".