The spatial distribution of salmon and steelhead redds and optimal sampling design
Bibliographic record
Abstract
Redd surveys are used extensively to estimate spawner population size for Pacific salmon (Onchorynchus spp.). Because redds tend to be spatially aggregated, estimates of total redds based on subsamples of the potential spawning grounds can be uncertain unless the spatial structure is accounted for. Here we use known redd locations for three populations over several years to compare five different probability sampling designs through simulation. The coefficient of variation (CV) for estimates based on simple random sampling was high, with values well over 15% when sampling a third of the reaches. Moving to a spatially balanced sampling design (generalized random tessellation stratified; GRTS) produced improvements in two of the three watersheds (16%–22% reduction in CV). Estimates based on a stratified GRTS design and a GRTS design that included a census of all reaches close to the peak count had higher accuracy, with an approximate CV of one-half to one-third of GRTS alone. We show how these improvements are predicted by theory and under which conditions the different approaches are likely to perform well.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".