Comparing a linear with a non-linear method for acoustic localization
Bibliographic record
Abstract
The performance of two different acoustic localization techniques is evaluated with signals from right whales in the Bay of Fundy.The methods are compared to the GPS localization error (114-273 m, N=3) through the use of played back whale calls.The linear approach underestimates the source location error (22 m, N=3), whereas the non-linear approach exaggerates the error (462-1166 m, N=3).The linear approach may render unrealistic error bounds because of the inherent non-linear properties of the localization problem.The non-linear approach may exaggerate error bounds by choosing the wrong cross correlation peak for the time-of-arrival difference measurements.Whereas the GPS localization error was always contained within the non-linear error bounds it was never contained within the linear error localization bounds.This indicates that the non-linear approach can give more realistic error estimates, especially in situations where the sound path geometry is unknown.[Work supported by the Office of Naval Research and the Oticon Foundation.]s o m m a i r eLa performance de deux méthodes différentes de localisation acoustique est évaluée à partir de la localisation acoustique des baleines franches dans la Baie de Fundy.Les méthodes sont comparées à l 'erreur de localisation GPS (114-273 m) à partir de vocalisations de baleines franches préenregistrées.L 'approche linéaire sous-estime l 'erreur de localisation de la source sonore (22 m), alors que l'approche non-linéaire surestime l 'erreur (462-1166 m).L 'approche linéaire rend irréaliste la marge d 'erreur possible à cause des propriétés non-linéaires du problème de localisation.L 'approche non linéaire exagère la marge d 'erreur, ce qui est expliqué par le choix du mauvais maximum de corrélation croisée des mesures de différences de temps d'arrivée.Toutefois, l'erreur de localisation GPS était toujours contenue à l'intérieur d 'une marge d 'erreur non-linéaire et n 'était jamais contenue à l 'intérieur d 'une marge d 'erreur linéaire de localisation.Ceci indique que l 'approche non-linéaire peut donner des erreurs d 'estimation plus justes, spécifiquement dans les situations où la trajectoire du son est inconnue.[Travail supporté par l 'Office of Naval Research et la Oticon Foundation.]
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".