An integrated acoustic remote sensing and communications system for tidal front mapping
Bibliographic record
Abstract
A fundamental need exists in ocean science for accurate mapping of spatial and temporal variability of oceanographic parameters. The Autonomous Oceanographic Sampling Network (AOSN) concept offers this capability by combining an acoustic remote sensing and communication network with a fleet of AUVs equipped with sensors. In June of 1996, an AOSN was deployed in Hare Strait, British Columbia to monitor an active tidal front. Each system includes a tomography source, a communication source, a sixteen channel hydrophone array for receiving acoustic tomography and communications, and an array navigation system for monitoring hydrophone positions. The main electronics package on each mooring is comprised of a PC, DSPs, and analog-to-digital converter boards. Each mooring in the network is controlled in real time via a wireless Ethernet link to a base station located approximately 16 km away on Vancouver Island. Here, all data are logged and analyzed as the experiment is dynamically configured to monitor the evolving front. Some of the unique aspects of both the hardware and software system designs as well as preliminary results from the experiment are described.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".