METHODS FOR IMPLEMENTING NEURAL NETWORK HYDROACOUSTICS ALGORITHMS BASED ON HETEROGENEOUS HARDWARE PLATFORM GRIFON
Bibliographic record
Abstract
Recently, there has been an increase of interest in creating new neural network hydroacoustics algorithms, for example, test sites in the Arctic are being created to evaluate the effectiveness of target detection and to ensure the safety of the Canadian North. The open architecture allows you to adapt hardware to new threats or economic interests. Not only the equipment of radiation and reception of hydroacoustic systems is improved, but also their processing. The article discusses the ways of improvement and the principles of building specialized computers based on the GRIFON hardware platform, providing control and signal processing in mobile and stationary sonar systems. We consider an example of creating a complex that demonstrates the capabilities of the equipment and the prospects for creating calculators with high parameters and minimal mass‑dimensional characteristics. Described in detail the structure and principles of the mathematical software of specialized computer. Comparative characteristics of the proposed algorithms that can be used to optimize signal processing paths and the volume of digital equipment are given.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".