Deploying action cameras to observe fish in shallow, ice-covered streams
Bibliographic record
Abstract
Winter is a stressful season for freshwater, stream-dwelling fish because ice decreases habitat area and creates habitat fragmentation, and cooler water temperatures lower fish metabolism. Small streams regularly become completely ice-covered, and common techniques used to study fish developed for open-water conditions are not easily modified. These winter conditions make it difficult to accurately observe fish behaviour in their natural habitat. This study evaluated remote underwater video cameras used to observe minnow behaviour in overwinter conditions. Waterproof action cameras (e.g. GoPros™ and Sony™ action cameras) were lowered into the water column from the channel ice and set to record for 30-minute intervals, and with a modified antenna, Bluetooth™ connected cameras facilitated real-time observations to check and ensure camera position and function. Advances in video camera technology have allowed high quality video to be captured with inexpensive equipment (∼$500 CDN for camera, case, memory card and back-up batteries), such as small, portable action cameras that are now readily available. This technique was effective at observing fish behaviour, communities and habitat preference during the winter in small, ice-covered streams, which is important for water resource and fisheries management, conservation biology and stream restoration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".