Passive acoustic ship detection performance near the Port of Sept-Îles, Quebec
Bibliographic record
Abstract
An acoustic recorder was deployed near the Port of Sept-Îles, Quebec in the fall of 2020 and collected six months of data on a four-channel orthogonal array. The system, a JASCO C-lander, operated on a duty cycle consisting of 340s of data recorded at 32 kHz sampling rate, 1 min of data recorded at 256 kHz sampling rate followed by 500s of sleep. Data were stored on SD memory cards for post-retrieval analysis. Vessels were detected using narrowband tonals produced by their propulsion system and other rotating machinery and the sound pressure level (SPL) for each minute of data in the 40–315 Hz shipping frequency band was then computed. A 10min shoulder period before and after the detection was then searched for the highest 1 min SPL which was identified as closest point of approach (CPA) time for each acoustic contact. Vessel track data from the automatic identification system (AIS) were used to compute CPAs for vessels carrying an AIS transponder during the deployment period. A comparison of the two results was used to identify missed and false detections, and to assess the algorithm’s performance. Recommendations for implementing an improved detection approach will be discussed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".