Bibliographic record
Abstract
Active sonar echoes from man-made objects and those from naturally occurring features in certain coastal environments can be difficult to distinguish when using traditional sonar processing techniques and visual displays. An approach being investigated at Defence R&D Canada-Atlantic for addressing this challenge is to exploit the capability of human hearing for discriminating between these two classes of echoes. Part of the work consisted in producing a human-performance baseline. Two human listening tests were designed and carried out. Both used stimuli that were generated from a sample of echoes recorded during actual sonar experiments at sea. In the second test, a 500-Hz high-pass filter was applied to the stimuli. Quantitative data from the rating-exercise portion of the tests were used to produce receiver-operating-characteristic (ROC) curves for modeling how well the participants could distinguish the two classes of echoes. For both tests, the results show that the listeners could hear differences, but performance was significantly better in the first test. Qualitative data collected from the questionnaire portion of the tests helped to interpret some of the performance results.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".