Effectiveness of Night Snorkeling for Estimating Steelhead Parr Abundance in a Large River Basin
Bibliographic record
Abstract
Abstract The refinement of methodologies for estimating the abundance of juvenile salmonids in small streams has been an important area of fisheries research, but the development of methods suitable for larger streams has received insufficient attention. Using a novel approach for obtaining marked populations of fish, we evaluated the effectiveness of night snorkeling counts for estimating the abundance of steelhead Oncorhynchus mykiss parr in streams within a large river basin. Sampled streams ranged from headwater tributaries with wetted widths of less than 10 m to a large, main-stem river with a wetted width approaching 100 m. Estimates of snorkeling detection probability for steelhead parr were consistently high (overall mean = 0.65) and exhibited moderately low variability among sites (coefficient of variation = 0.24). Detection probability exhibited a dome-shaped relationship to fork length and declined with increasing cross-sectional site area. The high and relatively consistent detection probabilities we estimated indicate that night snorkeling can be an effective technique for estimating the basinwide abundance of steelhead parr.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".