Estimating observer error and steelhead redd abundance using a modified Gaussian area-under-the-curve framework
Bibliographic record
Abstract
This study examined how a suite of habitat and environmental variables relate to the ability of a stream surveyor to identify (observer efficiency) and distinguish (observer accuracy) steelhead (Oncorhynchus mykiss) redds from other stream features. Two existing spawning survey protocols that included one or two redd observers were used to develop models to estimate redd observer error. In most cases, steelhead redd abundances using raw redd counts were underestimated. Mean annual rates of observer efficiency ranged from 0.44 to 0.57, and observer accuracy ranged from 0.67 to 0.83. Regardless of the observer error model used, adjusted annual redd abundance estimates were generally unbiased (range 1.6–0.6 redds). A Gaussian area-under-the-curve methodology that incorporates redd count data and observer error rates was used to generate unbiased estimates of steelhead redd abundance in the Wenatchee (170 redds, coefficient of variation (CV) = 44%) and Methow (106 redds, CV = 41%) rivers. Unbiased estimates of redd abundance will help inform new population viability analyses to better prioritize those populations with the greatest conservation need.
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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 | 0.039 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".