Integrating Multiple Intrinsic Markers to Infer Habitat Use of Sockeye Salmon Stocks (Oncorhynchus nerka) in the North Pacific Ocean
Bibliographic record
Abstract
Pacific salmon (Oncorhynchus spp.) are renowned for their high mobility, complex population structure, and homing to natal spawning grounds.Following a year or more in freshwater and estuarine habitats, sockeye salmon (O.nerka) in British Columbia, for example, are thought to move north and north-west along the coast during their first summer and winter at sea before migrating offshore into the North Pacific Ocean.During their marine phase, they have the potential to occupy a vast range between the Aleutian Islands and the Washington/Oregon coasts before returning to their home rivers and spawning grounds 2-6 years later (Tucker et al. 2009;Farley et al. 2018).Recent studies on salmonids have greatly advanced our understanding of the timing of ocean entry, natal origins, and habitat use during the first year of life (e.g., Barnett-Johnson et al. 2008;Miller et al. 2010;Volk et al. 2010;Stocks et al. 2014;Campbell et al. 2015).However, much less is known about their habitat use, distribution, and migration patterns after their first autumn at sea-a period that comprises the majority of their lives.The great obstacle to resolving ocean habitat use is the logistical challenge of capturing/tracking salmon on the high seas.Yet, tracing movement pathways through the North Pacific is essential to understanding how salmon populations are and will be impacted by regionally dynamic changes in ocean conditions.To advance techniques for tracing complex oceanic movements of salmon, we evaluate the utility of integrated intrinsic genetic, chemical, and microstructural markers to infer habitat use, movement patterns, and their relationship with relative growth rates of O. nerka during their marine phase.
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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 | 0.001 | 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 | 0.001 | 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".