Disentangling Population Level Differences in Juvenile Migration Phenology for Three Species of Salmon on the Yukon River
Bibliographic record
Abstract
Migration phenology influences many important ecological processes. For juvenile Pacific salmon, the timing of the seaward migration from fresh to marine waters is linked to early marine survival and adult returns. Seaward migration phenology is determined by interactions between the intrinsic attributes of individual species and environmental factors that are acting upon them. Temperature and discharge are two factors of the freshwater environment that have been shown to influence intra- and interannual variation in juvenile salmon phenology, but these factors may affect the migrations of sympatric species differently. Understanding how variations in phenology change with environmental heterogeneity is a critical first step in evaluating how the future climate may affect salmon. This is especially crucial for high-latitude rivers, where the pace of climate change is nearly twice as rapid as it is for more temperate areas. This research investigates the influence of river conditions on the seaward migration phenology of Chinook, chum, and coho salmon in the Yukon River. The results identified species-specific differences in the factors affecting migration duration, concentration, and skew and provide a starting point for a more detailed examination of how phenological variability may affect the temporal matching of juvenile salmon with biological resources and environmental conditions for optimal survival.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".