DeepVision: a stereo camera system provides highly accurate counts and lengths of fish passing inside a trawl
Bibliographic record
Abstract
The DeepVision stereo camera system collects a continuous record of colour images of all fish passing inside the extension of a trawl. Ninety-eight percent of 1729 fish captured while trawling could be identified to species from images, and lengths could be estimated from the images of 96% of the fish identified. A landmark distance technique developed to estimate lengths from images containing incomplete, curved, or obscured fish introduced <1% error for the majority of individuals (maximum 5% error). The technology can greatly increase the scope of information collected during trawl sampling, including documenting fine-scale distribution of individual fish and species overlap. Such information can aid in interpreting acoustic data and fine-scale community composition and could be collected with an open codend trawl, greatly reducing sampling mortality. Images are easily archived, providing an opportunity to quality check the raw data and revisit datasets originally collected for different purposes. Adaptation of the technology for commercial fisheries could reduce the catch of unwanted species and sizes.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".