How useful is underwater video as a fisheries assessment tool in temperate freshwater ecosystems?
Bibliographic record
Abstract
ABSTRACT Objective Remote underwater video (RUV) is a noninvasive survey technique that has long been used in marine ecosystems and is now gaining interest among freshwater fisheries scientists. Our objective was to test the efficacy of RUV for assessing fish species richness and abundance in the littoral zone of two water bodies in central Ontario, Canada. Methods With 133 deployments, we used RUV, minnow traps, snorkel surveys, and beach seine netting (the latter in river sites only) to survey fish assemblages and compared their estimates of species richness using species accumulation curves and generalized linear models. We compared maxN (a conservative estimate of abundance from RUV) to estimates of fish density from seine netting. Results We found that RUV estimated similar or higher species richness when compared with minnow traps and snorkeling but underestimated species richness relative to seine netting, which captured several uncommon small-bodied fishes that RUV failed to detect. The maxN was correlated with density estimates from seine netting for only 4 out of 11 species and life stages, suggesting that maxN is only a useful index of abundance for some species. Remote underwater video was sufficiently sensitive to detect a major interannual change in abundance of the invasive Round Goby Neogobius melanostomus, but the magnitude of change was much lower than the increase in density estimated by seine netting. Conclusions Collectively, the data reported here suggest that RUV is a useful tool, especially when used in conjunction with other methods or where other fisheries survey methods (e.g., electrofishing and netting) are not possible, such as for community groups interested in developing their own monitoring programs.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".