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 é s u m éEn septembre 2002, cinq hydrophones ancrés au fond marin dans la Baie de Fundy ont enregistré des données acoustiques échantillonnées à 1200 Hz pour une durée de 165.6 h.Des différences de temps d 'arrivée pour 15 sons de baleines franches (5 « coups de feu », 10 tonals) ont été déterminés par corrélation croisée de spectrogrammes de densité spectrale logarithmique (i.e.dB re 1 |iPa2/Hz).La localisation des sources sonores a été estimée à partir des intersections d 'hyperboles linéairement indépendantes de différences temporelles égales pour différentes paires d 'hydrophone s.La moyenne quadratique (RMS) de l 'erreur de localisation a été examinée en utilisant trois vitesses de son.L 'erreur RMS moyenne la plus basse (0.85 km) a été 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 à partir de l'hydrophone central du réseau étaient de 10 et 30 km respectivement.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".