Spatial variability of stable isotopes and fossil pigments in surface sediments of Alaskan coastal lakes: Constraints on quantitative estimates of past salmon abundance
Bibliographic record
Abstract
We quantified spatial patterns of stable isotopes of N and C (δ15N, δ13C) and fossil pigment concentrations in the uppermost 10 mm of sediment (,10 yr) from 74 profundal locations and three spawning‐stream discharge areas in Lake Nerka, southwest Alaska. Sediment δ15N (4.3% ± 0.7%) and δ13C (−26.3% ± 1.2%) varied directly (δ15N) or inversely (δ13C) with water column depth, whereas concentrations of most fossil pigments from algae were negatively correlated with depth. Sediment δ15N and δ13C were poorly correlated with either fossil pigment abundance or the local densities of spawning salmon. Instead, coastal nursery lakes appeared to integrate marine‐derived nutrients rapidly into lakewide nutrient pools, suggesting that while individual cores may be used to reconstruct whole‐lake salmon densities, habitat‐specific variations of past fish populations cannot be quantified reliably from sedimentary analyses.
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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 | 0.001 |
| 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 | 0.000 | 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".