Localisation of right whale sounds in the workshop bay of fundy dataset by spectrogram cross-correlation and hyperbolic fixing
Bibliographic record
Abstract
In September 2002, five ocean-bottom hydrophones recorded acoustic data in the Bay o f Fundy at 1200 Hz sampling frequency for 165.6 h.Arrival time differences for 15 right whale sounds (5 gunshots, 10 tonals) were determined by spectrogram cross-correlation o f logarithmic (i.e.dB re 1 p.Pa2/Hz) spectral densities.The sound source locations were estimated from the intersections o f the linearly independent equal time difference hyperbolae for different hydrophone pairs.The root-mean-square (RMS) localisation error was examined using three sound speeds.The lowest average RMS error o f 0.85 km was obtained for 1485 m/s, roughly 7 m/s less than the measured average sound speed.The non-gunshot sounds had greater localisation error than the gunshot sounds by 0.4 km.The mean and maximum ranges from the centre hydrophone in the array were 10 km and 33 km respectively. r s u m En septembre 2002, cinq hydrophones ancrs au fond marin dans la Baie de Fundy ont enregistr des donnes acoustiques chantillonnes 1200 Hz pour une dure de 165.6 h.Des diffrences de temps d 'arrive pour 15 sons de baleines franches (5 coups de feu , 10 tonals) ont t dtermins par corrlation croise de spectrogrammes de densit spectrale logarithmique (i.e.dB re 1 |iPa2/Hz).La localisation des sources sonores a t estime partir des intersections d 'hyperboles linairement indpendantes de diffrences temporelles gales pour diffrentes paires d 'hydrophone s.La moyenne quadratique (RMS) de l 'erreur de localisation a t examine en utilisant trois vitesses de son.L 'erreur RMS moyenne la plus basse (0.85 km) a t obtenue avec 1485 m/s, soit 7 m/s de moins que la mesure moyenne de la vitesse du son.Les vocalisations avaient une plus grande erreur de localisation que les sons coup de feu, soit 0.4 km de plus.Les distances moyennes et maximales partir de l'hydrophone central du rseau taient de 10 et 30 km respectivement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".