Localization of right whales using matched correlation processing
Bibliographic record
Abstract
The use of Matched Correlation Processing (MCP) for the localization of right whales was demonstrated. Model-based MCP techniques have been researched with the goal of improving the capability of passive sonar systems for localizing quiet underwater sources. The cross-correlations for a broadband source as measured with a pair of hydrophones in a horizontal array are matched with those generated with a correlation model for many candidate ranges and depth along a candidate bearing. These matches are carried out with a number of hydrophone pairs to form many range-depth ambiguity surfaces. A three-sensor horizontal array with an aperture of 125 m was deployed on the sea bottom in 68 m of water and the hydrophones were localized using an Array Element Localization (AEL) procedure. A light bulb was imploded at a depth of 41.5 m from hydrophone. Using light bulbs as simulated right whale gunshots, the technique was shown to work extremely well out to a range of about six water depths in an environment that has sufficient bottom reflections.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".