Measuring fish and their physical habitats: versatile 2D and 3D video techniques with user-friendly software
Bibliographic record
Abstract
Applications of video in fisheries research range from simple biodiversity surveys to three-dimensional (3D) measurement of complex swimming, schooling, feeding, and territorial behaviors. However, researchers lack a transparently developed, easy-to-use, general purpose tool for 3D video measurement and event logging. Thus, we developed a new measurement system, with freely available, user-friendly software, easily obtained hardware, and flexible underlying mathematical methods capable of high precision and accuracy. The software, VidSync, allows users to efficiently record, organize, and navigate complex 2D or 3D measurements of fish and their physical habitats. Laboratory tests showed submillimetre accuracy in length measurements of 50.8 mm targets at close range, with increasing errors (mostly <1%) at longer range and for longer targets. A field test on juvenile Chinook salmon (Oncorhynchus tshawytscha) feeding behavior in Alaska streams found that individuals within aggregations avoided the immediate proximity of their competitors, out to a distance of 1.0 to 2.9 body lengths. This system makes 3D video measurement a practical tool for laboratory and field studies of aquatic or terrestrial animal behavior and ecology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".