Side Scan Sonar and Underwater Discovery
Bibliographic record
Abstract
This presentation will take you on a journey of underwater discovery made possible by side scan sonar. An introduction on the theory of side scan sonar operation will be presented along with some of the amazing discoveries this technology has made possible. The journey will take us from the Arctic, to South America to Lochness and more. Presenter Bio For the past 34 years, Garry has been employed by Klein Associates of Salem, NH, the technology leader in Side Scan Sonar systems. He is a recognized expert in undersea search operations and travels the world providing consulting and training expertise to Navies and companies who have a critical underwater search need. Garry began his underwater search and survey career in 1972, doing side scan sonar surveys off the coast of Labrador, for CanDive/Oceaneering. His work has taken him to the Canadian Arctic with Dr. Joe MacInnis to locate the H.M.S. Breadalbane, off the coast of England with author Clive Cussler in search of John Paul Jones’ vessel, the Bon Homme Richard, and on countless other treasure and historic shipwreck.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".