Inferring sockeye salmon (<i>Oncorhynchus nerka</i>) population dynamics and water quality changes in a stained nursery lake over the past ∼500 years
Bibliographic record
Abstract
Historical and paleolimnological studies have demonstrated that environmental changes in the North Pacific can strongly affect sockeye salmon (Oncorhynchus nerka) abundances. Whether these marine shifts would be influential on sockeye salmon from all lake types, however, has not yet been studied. This study represents the first paleolimnological analysis of past sockeye salmon population dynamics in a stained nursery lake (Packers Lake, Alaska). We adopted a multiproxy approach to determine whether salmon-derived nutrients (inferred from δ15N) would be available for algal uptake (inferred from the diatom species responses) in this stained lake, as high concentrations of humics and iron are known to sequester phosphorus. The strong degree of coherency between δ15N and diatoms, however, suggests that salmon-derived nutrients were bioavailable and enhanced productivity. Overall, our indicators responded to changes in sockeye salmon abundances and volcanic ashfalls over the past ∼500 years. In a section of the core unaffected by tephras (AD ∼17701882), our record suggests that the number of sockeye salmon spawners fluctuated widely. Comparison of temporal shifts in inferred sockeye salmon abundances from Packers Lake with other clearwater nursery lakes reveals a broadly consistent pattern, likely influenced by past climatic changes.
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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 | 0.000 |
| 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 | 0.000 |
| 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".