Study on the human ability to aurally discriminate between target echoes and environmental clutter in recordings of incoherent broadband sonar
Bibliographic record
Abstract
Unacceptably high false-alarm rates due to the inability to discriminate between target echoes and environmental clutter are an issue for existing low-frequency active sonar systems operating in coastal environments. A research project at Defence R&D Canada—Atlantic is investigating the potential use of aural cues to tackle this challenge. One aspect of the project is to evaluate the human ability to aurally discriminate between target echoes and environmental clutter. The design and preliminary results from the study are presented here. Human subjects are presented with a series of sounds containing target echoes and clutter obtained from recordings of an incoherent broadband sonar experiment. The quantitative data collected in the study are the subjects’ decisions as to whether the echo heard was a target echo or clutter and their level of confidence associated with the decisions. Receiver-operating characteristic (ROC) analysis is used to produce a statistical model of the subjects’ performance. The study also includes a questionnaire: answers may prove useful in supporting the quantitative results and in providing a better understanding of the cues and decision techniques used by the subjects.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".