Environmental controls of phenology of high-latitude Chinook salmon populations of the Yukon River, North America, with application to fishery management
Bibliographic record
Abstract
Abstract Mundy, P. R., and Evenson, D. F. 2011. Environmental controls of phenology of high-latitude Chinook salmon populations of the Yukon River, North America, with application to fishery management. – ICES Journal of Marine Science, 68: 1155–1164. Phenologies of a number of anadromous fish species have been demonstrated to vary in concert with environmental factors that change with global warming, such as water and air temperatures. Anadromous fishery managers will need advice from models of phenology, or migratory timing, as functions of environmental factors in those harvest areas where annual migratory timing can vary sharply. Such models are also necessary to advise fishery managers on how and when global warming projections of the IPCC model ensemble should be factored into regulatory decisions. Specifically, we demonstrate that the annual timing of marine exit of Yukon River Chinook salmon (Oncorhynchus tshawytscha) at 63°N 165°W for 1961–2009 varied in close concert with modelled sea surface temperature, air temperature, and sea ice cover. The best linear model for 1961–2009 combines sea surface and air temperatures to explain 59% of the annual variability in migratory timing (ice cover is available only for 1970–2009). Changes in phenology of high-latitude Chinook salmon are expected in response to global warming. As average temperatures increase, the frequency of earlier migrations is expected to increase, making management of the fishery more challenging.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".