Characterization and testing of a new bistatic profiling acoustic Doppler velocimeter: The Vectrino-II
Bibliographic record
Abstract
Pulse-to-pulse coherent Doppler sonar systems have been commercially available for almost two decades now. These systems provide non-intrusive, high accuracy, low noise data in difficult environments. Pulse coherent profilers are also capable of measuring very small cell sizes and provide far more details of flow than standard Doppler systems. Multi-beam bi-static profiling systems allow measurements of velocity over a specified range of cells with each beam providing data from closely spaced measurement volumes, thereby removing the need for assumptions of flow homogeneity as required for mono-static systems with diverging beams. While a few bi-static profiling prototype systems have been demonstrated, there have been no commercial platforms available that provide a cost-effective, turn-key solution for providing three component data profiles with accompanying display and processing software tools. This paper will describe one such system, the Nortek Vectrino-II. A description of the instrument hardware and software capabilities will be followed by a discussion of some of the novel features and algorithms used by the instrument. Tow tank data and comparisons with a PIV system will be presented.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".