DeepVision: a stereo camera system provides highly accurate counts and lengths of fish passing inside a trawl
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it