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Record W2752702595 · doi:10.14430/arctic4665

Evaluating Evidence for Historical Anadromous Salmon Runs in Eklutna Lake, Alaska

2017· article· en· W2752702595 on OpenAlexvenueno aff
Michael G. Loso, Bruce P. Finney, Richard R. Johnson, Rick Sinnott

Bibliographic record

VenueARCTIC · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsVarveGeologyFish migrationPaleolimnologyFish <Actinopterygii>Hydrology (agriculture)OceanographyEnvironmental scienceFisherySedimentPaleontologyHolocene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.108
GPT teacher head0.371
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes1
Has abstractyes

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