EVALUATING STATISTICAL APPROACHES TO QUANTIFYING JUVENILE CHINOOK SALMON HABITAT IN A REGULATED CALIFORNIA RIVER
Bibliographic record
Abstract
Decisions on managed flow releases in regulated rivers should be informed by the best available science. To do this, resource managers require adequate information regarding the tradeoffs between alternative methodologies. In this study, we quantitatively compare two competing multivariate habitat models for juvenile Chinook salmon (Oncorhynchus tschawytscha), a highly valued fish species under serious decline in a large extent of its range. We conducted large-scale snorkel surveys in the American River, California, to obtain a common dataset for model parameterization. We built one habitat model using Akaike Information Criterion analysis and model averaging, ‘model G’, and a second model by using a standard method of aggregating univariate habitat models, ‘model A’. We calculated Cohen's kappa, percent correctly classified, sensitivity, specificity and the area under a receiver operator characteristic to compare the ability of each model to predict juvenile salmon presence and absence. We compared the predicted useable habitat of each model at nine simulated river discharges where usable habitat is equal to the product of a spatial area and the probability of habitat occupancy at that location. Generally, model G maintained greater predictive accuracy with a difference within 10% across the diagnostic statistics. Two key distinctions between models were that model G predicted 17.2% less useable habitat across simulated flows and had 5% fewer false positive classifications than model A. In contrast, model A had a tendency to over predict habitat occupancy and under predict model uncertainty. The largest discrepancy between model predictions occurred at the lowest flows simulated and in the habitats most likely to be occupied by juvenile salmon. This study supports the utility and quantitative framework of Akaike Information Criterion analysis and model averaging in developing habitat models. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.032 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".