Hierarchical Bayesian estimation of unobserved salmon passage through weirs
Bibliographic record
Abstract
We developed a hierarchical Bayesian model (HBM) to estimate missing counts of Chinook salmon (Oncorhynchus tshawytscha (Walbaum in Artedi, 1792)) at a weir on the Kogrukluk River, Alaska, between 1976 and 2015. The model assumed that fish passage during a breach of the weir was typical of passage during normal operations. Counts of fish passing the weir were missing for some days during the runs, or only partial counts for a given 24-hour period were available. The HBM approach provided more defensible estimates of missing data and total escapement than ad hoc or year-by-year model estimates, because estimates of passage for a given year were informed not only by counts for the current year, but also by counts for all previous years. The results of the HBM yielded less variable estimates of escapement than did ad hoc or year-by-year model estimates. The HBM represents a standardized approach to estimate missing counts and total escapement and eliminates the need for ad hoc estimates of missing counts. The model also provides managers with a measure of uncertainty around estimates of escapement and around estimates of hyper-parameters to define run curves in subsequent years with incomplete fish counts.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".