Phenology and Fraser River sockeye salmon marine survival
Bibliographic record
Abstract
Inspired by the pioneering work of Dr. Bill Peterson who demonstrated the utility of ocean indicators at predicting survival of coho and chinook salmon in the Columbia River, we investigated whether the phenology of primary productivity could explain variable marine survival of Fraser River sockeye salmon. Building on a study that had found a strong correlation between satellite-derived spring chlorophyll concentrations in Queen Charlotte Sound (British Columbia) and smolt survival, we hypothesized that smolt migration phenology could help to explain interannual survival differences among years. Applying a new migration model to 18 years of smolt migration data from Chilko Lake demonstrated that interannual differences in smolt migration timing were organized in up to 3 pulses of abundance with a general trend by the largest peak toward earlier peak migration dates over the time series (1998–2016). Analysis of satellite-derived fluorescence line height data within Queen Charlotte Sound identified 4 productivity domains through which most young sockeye salmon would migrate. Each domain had distinct seasonal productivity patterns. With these data, we were unable to demonstrate significant correlations between spring bloom dates in these domains and smolt marine survival, or between smolt migration timing and marine survival. Having separate survival estimates for each pulse and phenological indicators of the sockeye salmon prey base might improve our ability to test the hypothesis that phenology matters to sockeye salmon in the Queen Charlotte Sound region.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".