Evaluating Evidence for Historical Anadromous Salmon Runs in Eklutna Lake, Alaska
Bibliographic record
Abstract
We assessed historical presence of sockeye salmon in Eklutna Lake, Alaska, prior to construction of a diversion dam on the downstream Eklutna River in 1929, using nitrogen stable isotopes measured in a lacustrine core 93 cm long. Sediments in the core were dated using varve counts, verified by 210Pb and 137Cs measurements. The basal date of the core was AD 1859, and varves became slightly thinner and less distinct after 1929. Sediments were primarily clastic with carbon content below 1%. Nitrogen isotope values were generally low and stable throughout the core, ranging from 1.5‰ to 2.5‰. There is no statistical evidence for a change in isotopic composition after emplacement of the dam. In light of published evidence from oral history, cultural records, and habitat relationships that suggest sockeye salmon could have been present in the lake before 1929, we conducted a simple sensitivity test to assess the possibility that a small salmon run may have gone undetected by our technique. We found that a salmon run of up to 1000/year, and potentially as many as 15 000/year, would be possible without noticeably altering the measured isotopic composition of the sediments in Eklutna Lake. Our results provide no evidence that such runs occurred, but do not preclude the possible existence of a relatively small sockeye fishery in Eklutna Lake before 1929.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".