The influence of hydrology and waterway distance on population structure of Chinook salmon <i>Oncorhynchus tshawytscha</i> in a large river
Bibliographic record
Abstract
Adult Chinook salmon Oncorhynchus tshawytscha navigate in river systems using olfactory cues that may be influenced by hydrologic factors such as flow and the number, size and spatial distribution of tributaries. Thus, river hydrology may influence both homing success and the level of straying (gene flow), which in turn influences population structure. In this study, two methods of multivariate analysis were used to examine the extent to which four indicators of hydrology and waterway distance explained population structure of O. tshawytscha in the Yukon River. A partial Mantel test showed that the indicators of hydrology were positively associated with broad-scale (Yukon basin) population structure, when controlling for the influence of waterway distance. Multivariate multiple regression showed that waterway distance, supplemented with the number and flow of major drainage basins, explained more variation in broad-scale population structure than any single indicator. At an intermediate spatial scale, indicators of hydrology did not appear to influence population structure after accounting for waterway distance. These results suggest that habitat changes in the Yukon River, which alter hydrology, may influence the basin-wide pattern of population structure in O. tshawytscha. Further research is warranted on the role of hydrology in concert with waterway distance in influencing population structure in Pacific salmon.
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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 | 0.000 | 0.001 |
| 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 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".