Accuracy of Diver Counts of Fluvial Rainbow Trout Relative to Horizontal Underwater Visibility
Bibliographic record
Abstract
Abstract We investigated the effects of variation in underwater visibility on the accuracy of diver counts of rainbow trout Oncorhynchus mykiss in the Salmo River, British Columbia, over a 3-year time period. A four-man team of divers, drifting in a downstream direction, made periodic counts of trout along a study reach in which radio-tagged fish that had also received a visual mark were present. Observer efficiency of divers (number of tags seen relative to the number known to be present) was significantly related to horizontal underwater Secchi disk visibility during 2002 and 2003 but only poorly so for the first year of the study in 2001. Overall, horizontal visibility in the 3 years' combined data set was significantly related to observer efficiency, explaining 62% of the variation. Diver counts of untagged trout confirmed these patterns. Diver counts of trout greater than 30 cm and greater than 40 cm showed precise, significant relationships with horizontal visibility for both 2002 and 2003. Horizontal visibility changes explained 94% and 93% of the variation in counts of trout greater than 30 cm and greater than 40 cm, respectively, in 2002 and 94% and 87% of the variation for the same size categories in 2003. The poor-quality, nonsignificant relationships between counts of trout and visibility in 2001 were consistent with the poor observer efficiency relationship for that year as estimated from the observations of the radio-tagged fish. Taken together, these poor-quality relationships for the first year of the study point to either crew inexperience or some other factor(s) as an important source of error in addition to water clarity.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".