Ecological, landscape, and climatic regulation of sediment geochemistry in North American sockeye salmon nursery lakes: Insights for paleoecological salmon investigations
Bibliographic record
Abstract
We used multiple linear regressions across a Northeast Pacific region 56‐lake set to examine the influence of sockeye salmon spawner densities and limnological, climatic, and watershed characteristics on sediment stable nitrogen isotope (δ15N) and C:N signatures, geochemical proxies used in paleolimnological reconstructions of prehistoric salmon abundances. Across all sites (n = 56), sedimentary C:N was primarily related to variables reflecting allochthonous organic matter and nutrient fluxes, with 67= of the variance explained by watershed vegetation type, watershed area, mean slope, and salmon spawner densities. In a subset of sites with nutrient data (n = 41), 63= of variance in C:N was explained by precipitation and watershed vegetation type. Sediment δ15N was most strongly related to salmon spawner densities, but C:N and watershed area : lake area ratios explained significant residual variance (total 55=). In sites with nutrient data, salmon spawner densities, vegetation type, and spring nitrate explained 62= of the variance in d15N. Sediment C:N and δ15N values exhibited distinct interregional variation, typically varying inversely. Regional d15N‐salmon density relationships (regression slopes) varied strongly with total annual precipitation (r2 = 0.89, p = 0.016, n = 5), suggesting watershed organic matter and nutrient loading vary predictably in relation to regional biogeoclimatic conditions. Our findings demonstrate that paleolimnological analyses are useful for quantitative reconstruction of past salmon densities; however, inferences regarding past salmon populations must consider the factors regulating influxes of nitrogen from watershed sources.
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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 | 0.000 | 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".