Accounting for Demographic and Environmental Stochasticity, Observation Error, and Parameter Uncertainty in Fish Population Dynamics Models
Bibliographic record
Abstract
Abstract Bayesian hierarchical state-space models are a means of modeling fish population dynamics while accounting for both demographic and environmental stochasticity, observation noise, and parameter uncertainty. Sequential importance sampling can be used to generate posterior distributions for parameters, unobserved states, and random effects for population models with realistic dynamics and error distributions. Such a state-space model was fit to the Sacramento River winter-run Chinook salmon Oncorhynchus tshawytscha population, where a key objective was to develop a tool for predicting juvenile out-migration based on multiple sources of data. One-year-ahead 90% prediction intervals based on 1992−2003 data, while relatively wide, did include the estimated values for 2004. Parameter estimates for the juvenile production function based on the state-space model formulation differed appreciably from Bayesian estimates that ignored autocorrelation and observation noise.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".