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].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".