Source amplitude spectral information in matched-field localization.
Bibliographic record
Abstract
This paper examines a variety of approaches to make use of knowledge of the relative amplitude spectrum of an acoustic source (but no knowledge of the phase spectrum) in multifrequency matched-field processing for source localization. A common example of this procedure involves cases where the source amplitude spectrum can be considered flat over the frequency band of interest. The primary issue is how to combine the information of complex acoustic fields at multiple frequencies, given the unknown phase spectrum. Approaches examined include maximum-likelihood phase estimation, pair-wise processing, and phase rotation to zero the phase at a specific sensor or to zero the mean phase over the array. The performance of the various approaches (processors) is quantified in terms of the probability of localizing the source within an acceptable range-depth region, as computed via Monte Carlo sampling over a large number of random realizations of noise and of environmental parameters. Processor performance is compared as a function of signal-to-noise ratio, number of frequencies, number of sensors, and number of time samples (snapshots) included in the signal averaging.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".