Caveats on the use of paleolimnology to infer Pacific salmon returns
Bibliographic record
Abstract
The ability of paleolimnology to reconstruct historical sockeye salmon (Oncorhynchus nerka) abundance was assessed at Fraser Lake, an important nursery lake in the interior of British Columbia (BC), Canada. Multiple sediment proxies of lake production, as well as nitrogen and carbon stable isotopes, all portray relatively complacent stratigraphies, despite well‐monitored changes in salmon returns over the most recent 60 years. The separation of autochthonous from total sediment organic matter did not clarify the identification of the nitrogen isotopic fingerprint of marine‐derived nutrients (MDN). Slight shifts in diatom assemblages are better interpreted as responses to either early human activities in the catchment or post‐Little Ice Age climate warming. The low proportion of MDN in the lake's annual budget (3% N and 7% P) is the probable reason their influence is not expressed in lake sediments. Thus, paleolimnology is incapable of providing unequivocal inferences concerning historical salmon abundances in Fraser Lake, in contrast to lakes from southern Alaska, where the same techniques have yielded unambiguous results. Sediment MDN proxies, together with catchment characteristics and escapement data, were collated for an additional nine sockeye nursery lakes spanning southern Alaska to southern BC. Primary production and biogeochemical cycling in Alaskan nursery lakes appears largely driven by MDN from sockeye returns, whereas BC lakes are more strongly influenced by allochthonous organic matter and lake‐water residence times. Alaskan lakes possess the limnological features that are prerequisite for meaningful salmon reconstructions using paleolimnology, while lakes from southern BC do not. This does not suggest that MDN are unimportant in southern BC lakes but rather that they are not readily disentangled from other factors that shape the paleolimnological record.
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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 | 0.094 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".