Predicting fish abundance using single-pass removal sampling
Bibliographic record
Abstract
Three-pass removal data for juvenile rainbow trout (Oncorhynchus mykiss) along bank areas of the Henrys Fork of the Snake River, Idaho, were used to construct a mean capture probability (MCP) model to predict abundance from single-pass catch data. We evaluated the MCP model by simulation. The precision of the MCP model was poor when predicting abundance within a specific bank unit. MCP model prediction intervals were about 7.5 times greater than three-pass removal intervals. However, the MCP model performed about the same as three-pass removal for predicting total abundance in a river section from multiple bank samples. We evaluated how the MCP model can be used to improve precision of total abundance estimates. Reallocating effort to sample 150 bank units by single-pass removal rather than 50 bank units by three-pass removal resulted in a 48% increase in prediction interval precision for a simulated population of 10 000 fish. Precision also increased when allocating effort to sampling more bank units of smaller length versus fewer bank units of longer length. Sampling 1500 m of bank as one hundred 15-m bank units increased precision by about 28% versus sampling fifty 30-m bank units and by about 50% versus sampling twenty-five 60-m bank units.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".