Surveillance arrays for shallow water: Comparison of planar vs. line bottomed arrays
Bibliographic record
Abstract
A number of different array architectures, including horizontal and vertical line arrays and planar arrays, are currently being developed for shallow water applications. An objective of the work is to assess the performance of the different array architectures. To achieve this the arrays were tested during a sea trial (RDS-2) that took place in the Timor Sea in November 1998. This paper compares the broadband detection performance of two designs of array, a planar array (Octopus) and a Horizontal Linear Array (ULRICA HLA), at the RDS-2 site. Noise statistics and signal threshold levels presented here are obtained from ambient noise data. Significant differences in the dependence of threshold on azimuth are shown between the Octopus and ULRICA arrays and are attributed to the different geometries and hence beampatterns of the arrays. Signal data, obtained from a submerged sound source, are used in conjunction with the noise data to determine detection performance at a range of source levels. The results indicate that the detection performance of 16 element ULRICA and Octopus arrays is comparable at the RDS-2 site.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".