Residual Effects from Fish Wheel Capture and Handling of Yukon River Fall Chum Salmon
Bibliographic record
Abstract
Abstract Since 1996, U.S. Fish and Wildlife Service biologists have annually used fish wheels to capture migrating adult fall chum salmon Oncorhynchus keta in the main-stem Yukon River, Alaska, and estimated their abundance via mark–recapture methods. In each year of the study, the mark rate of captured fish at a site near Rampart has been substantially greater than rates observed at numerous locations upriver of that site. The factors most likely to cause the observed reduction in the mark rate are violations of mark–recapture model assumptions or the mortality of marked fish between the Rampart site and upriver locations. Results of studies conducted through 2000 were most consistent with the hypothesis of mortality. We investigate potential explanatory factors for the apparent reduction in mark rates at upriver locations using data collected during additional studies from 2001 to 2003. Results document that holding fish in submerged pens at the marking site negatively affects their ability to migrate for at least some time. No evidence of tag loss or spatial segregation within the mark–recapture study area was observed. A conclusion that some aspect of the capture and handling of fish elevates their mortality upriver of the mark–recapture study area seems well founded. However, holding of fish does not solely explain the reduction in mark rates at upriver locations, and other contributing factors remain unidentified. Researchers using fish wheels should be aware that the gear may be more harmful to fish than was previously thought and may bias estimators of some parameters such as abundance or migration speed.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".