Freshwater life-cycle timing of Pacific salmon and steelhead (Oncorhynchus spp.) in Canada
Bibliographic record
Abstract
Understanding species’ phenology and distribution is essential for mitigating anthropogenic disturbances and understanding climate change vulnerability. Pacific salmon (Oncorhynchus spp.) have complex life cycles that span freshwater and marine environments and unfold over many thousands of kilometers. Currently, there is no central repository of life-cycle timing for unique salmon and steelhead populations throughout their Canadian range. We have compiled a dataset of timing for key freshwater life-cycle events - fry migration from incubation to rearing grounds, juvenile migration from rearing grounds to the ocean, adult entry into rivers, and spawning - for salmon and steelhead trout from British Columbia and the Yukon, Canada. We summarize patterns across species and populations to improve understanding of when species are in different freshwater habitats but found significant data gaps in remote regions that may challenge environmental planning. The data and insight we provide allow for more detailed examination of how salmon and steelhead populations will be exposed to future climate changes and can be used to inform adaptive management of fisheries and mitigation during human development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".