Bibliographic record
Abstract
Single hydrophone passive acoustic ranging is the practice of estimating the range to an underwater sound source using acoustic recordings from a single receiver. We present a multi-path arrival-based approach to estimate the horizontal range between a submerged source:receiver pair in a deep ocean environment (in which multi-path arrivals can be resolved and modal dispersion is minimal). A cost function and optimization approach are presented that are automated and robust to some significant sources of noise (including multiple sources) and environmental uncertainty. While several multi-path ranging methods have been presented in past literature, an important contribution of our approach is that it can be fully automated, it relaxes the requirement that arrivals are labeled/identified a priori, and it can function in multiple-source scenarios. Underwater acoustic data recorded by the ALOHA Cabled Observatory (ACO) are processed using our single hydrophone ranging approach. The horizontal range to 296 airgun shots, recorded by ACO on October 5-6, 2018, is compared against the true range for each shot. Good agreement between the true and estimated ranges demonstrates the performance of the ranging approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".