Commercial fishing vessels, automatic acoustic logging systems and 3D data visualization
Bibliographic record
Abstract
Over the past five years we have investigated and used commercial fishing vessels and their associated acoustic hardware as platforms for acoustic surveying and data collection. During this period we developed an automated acoustic logging system that will simultaneously record data from the ship's existing sounder, sonar, and navigation systems. The system was designed to be self contained and easy to activate. Once calibrated, the vessel's vertical echo sounder can be used for quantitative fish biomass estimates in a manner similar to a scientific echo sounder. Sonar data are collected in the form of digital images with a navigation file header. Post processing, editing, and visualization tools were developed to scale the sonar images according to range setting and tilt angle. Thereafter, both the sounder and sonar data are combined into a 3D visualization package for presentation, observation, and school area estimates. Industry based acoustic surveys of herring spawning grounds have been used to estimate spawning stock biomass and for near real-time decisions regarding harvest levels in NAFO Statistical Division 4WX since 1997. Currently, there are eight systems deployed on commercial purse seiners within the region. For the past four years data from structured surveys and fishing excursions have played a key role in the assessment of herring spawning stock biomass. While the application of the technology has been driven by a stock assessment mandate, its potential use is more far reaching. The spatial nature the data means that detailed and quantitative studies of fish behaviour, vessel avoidance, fish distribution, and target area can be undertaken from commercial fishing vessels with the addition of minimal equipment. However, quantification of sonar images is restricted to area/volume estimates as no digital amplitude data are available from the commercial fishing units.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".