Dispersal and productivity of Chinook (<i>Oncorhynchus tshawytscha</i>) and coho (<i>Oncorhynchus kisutch</i>) salmon colonizing newly accessible habitat
Bibliographic record
Abstract
Following construction of a fish ladder at Landsburg Diversion Dam on the Cedar River, Washington, USA, in fall 2003, we used DNA-based parentage to identify second generation Chinook (Oncorhynchus tshawytscha) and coho (Oncorhynchus kisutch) salmon as recruits that were produced above the dam or “strays” dispersing into the new habitat that were produced elsewhere. For both species, strays colonized immediately but decreased as a proportion of the total run over time. Chinook salmon strays were more numerous in years when the species was more abundant below the dam and included a much larger proportion of hatchery origin salmon than did coho salmon. Productivity, calculated as the ratio of female recruits sampled at the dam to female spawners, exceeded replacement in all four coho salmon cohorts but only two of five Chinook salmon cohorts, leading to more rapid population expansion of coho salmon. However, estimates of fishing mortality and recruitment into the Cedar River below the dam substantially increased Chinook salmon productivity estimates. Our results demonstrate that Pacific salmon are capable of rapidly recolonizing habitat made accessible by restoration and emphasize the importance of demographic exchange with preexisting populations during the transition from recolonization to self-sustainability.
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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 | 0.000 |
| 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) | 0.001 | 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".