Bibliographic record
Abstract
Signal processing inherent to broadband Doppler current profiling systems has great potential for applications in fisheries monitoring. The large bandwidth (25%–50% of the carrier frequency) contains information that can be used in target characterization, the system determines current profiles and there is the potential to make direct measurements of fish swimming speed. The capabilities of these systems are explored: field data collected with an RD Instruments 307-kHz Workhorse Acoustic Doppler Current Profiler (ADCP) demonstrate observations of large herring schools, performance is also evaluated using observations made in a tow-tank facility. For large concentrations of fish, speed and direction can be determined but for lower concentrations of fish new data processing techniques are required. For the system configuration as tested, single ping estimates have a standard deviation as low as 10 cm s−1. This accuracy is substantially better than the accuracy of ≂40 cm s−1 expected and suggests that for isolated targets the approach is equivalent to fully coherent Doppler processing. There is a difficulty discriminating discrete targets because of the long pulse code sequences transmitted. [This research was funded by the Natural Sciences and Engineering Council of Canada, and by Fisheries and Oceans Canada.]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.009 | 0.003 |
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".