Assessing covariability among populations in the presence of intraseries correlation: Columbia River spring-summer chinook salmon (<i>Oncorhynchus tshawytscha</i>) stocks
Bibliographic record
Abstract
We assessed covariability among a number of spawning populations of spring-summer run chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin by computing correlations among several different types of spawner and recruit data. We accounted for intraseries correlation explicitly in judging the significance of correlations. To reduce the errors involved in computing effective degrees of freedom, we computed a generic effective degrees of freedom for each data type. In spite of the fact that several of these stocks have declined, covariability among locations using several different combinations of spawner and recruitment data indicated no basinwide covariability. There was, however, significant covariability among index populations within the three main subbasins: the Snake River, the mid-Columbia River, and the John Day River. This covariability was much stronger and more consistent in data types reflecting survival (e.g., the natural logarithm of recruits per spawner) than in data reflecting abundance (e.g., spawning escapement). We also tested a measure of survival that did not require knowing the age structure of spawners, the ratio of spawners in one year to spawners 4 years earlier. It displayed a similar spatial pattern.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".