No Nets, No Shocks, No Problem: Assessing Replicability and Disturbance Effects in Fish Monitoring Using Remote Video Cameras in Low Order Streams
Bibliographic record
Abstract
Conventional fish population sampling methods such as electrofishing and netting, pose risks to fish and are often restricted to small, shallow headwater streams—especially where endangered species may be present. Additionally, non-capture surveying (e.g., snorkelling) can disturb fish and make observation more challenging. This study evaluates the effectiveness and reproducibility of remote underwater video (RUV) surveys in a shallow (<0.5 m deep), freshwater stream. Additionally, fish response to disturbances (e.g., shadows, noise, surface disruptions) were characterized. Fish abundance was estimated by maxN (maximum number of individuals observed in a single frame) and used multiple cameras placed in the same habitat (pool). Findings indicated a high consistency in maxN when fish numbers were low (<5 individuals), with increasing variability at higher numbers (>15 individuals). This suggests that single camera setups can reliably detect minimum abundance. Fish responses to four disturbances (e.g., shadows, noise, surface disruptions, mink) were noted throughout. Typically, these responses were short-lived, with fish returning to pre-disturbance maxN values within minutes, with the most significant response to researcher-induced disturbance occurring immediately after RUV deployment. Overall, RUVs proved effective for passive, non-capture fish monitoring in shallow, sensitive habitats, producing replicable data with minimal impact caused by researcher disturbance. This technique can be added to our toolboxes for studying small-bodied fishes in challenging environments.
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How this classification was reachedexpand
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".