Side Scan Sonar for Hydrography - An Evaluation by the Canadian Hydrographic Service
Bibliographic record
Abstract
Hydrographic surveys have improved in accuracy and efficiency over the last few decades with advances in electronics and data processing. Electronic positioning systems with automatic data loggers now make it possible to survey accurately at greater speed. Improved data processing systems eliminate the time-consuming, laborious task of scaling and plotting. The modern surveyor, however, is still plagued with the lack of knowledge of what lies between his sounding lines. Sonar developments promise to improve this situation as commercial equipment becomes available. Omnidirectional scanning sonars can view large areas of the bottom and display the features on a CRT display; searchlight type sonars yield range, azimuth and depression angle with the potential of making depth measurements far removed from the survey vessel; multiple beam sonars simultaneously sound sectors along the vessel path and side-looking sonars delineate features of the bottom on wide swaths, either side of the survey craft. This paper deals with the latter type, the dual side-scan sonar, specifically the type produced by E.G. & G. and Klein Associates of the United States. The principles of operation are presented, the results of an evaluation are given, and the use of the sonar over a field survey season is outlined.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".