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Record W2144559204 · doi:10.1016/j.yqres.2008.05.001

Timing and cause of water level fluctuations in Kluane Lake, Yukon Territory, over the past 5000 years

2008· article· en· W2144559204 on OpenAlexafffundabout
Janice Brahney, John J. Clague, Brian Menounos, W.D. Edwards Thomas

Bibliographic record

VenueQuaternary Research · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of WaterlooUniversity of Northern British ColumbiaSimon Fraser University
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaUniversity of Northern British Columbia
KeywordsGeologyGlacierHolocenePhysical geographyPeriod (music)Radiocarbon datingHydrology (agriculture)Shelf iceOceanographyGeomorphologyGeographyPaleontologyCryosphereIce stream

Abstract

fetched live from OpenAlex

Abstract We reconstructed late Holocene fluctuations of Kluane Lake in Yukon Territory from variations in bulk physical properties and carbon and nitrogen elemental and isotopic abundances in nine sediment cores. Fluctuations of Kluane Lake in the past were controlled by changes in climate and glaciers, which affected inflow of Slims and Duke rivers, the two largest sources of water flowing into the lake. Kluane Lake fluctuated within a narrow range, at levels about 25 m below the present datum, from about 5000 to 1300 cal yr BP. Low lake levels during this interval are probably due to southerly drainage of Kluane Lake to the Pacific Ocean, opposite the present northerly drainage to Bering Sea. Slims River, which today is the largest contributor of water to Kluane Lake, only rarely flowed into the lake during the period 5000 to 1300 cal yr BP. The lake rose 7–12 m between 1300 and 900 cal yr BP, reached its present level around AD 1650, and within a few decades had risen an additional 12 m. Shortly thereafter, the lake established a northern outlet and fell to near its present level.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.153
GPT teacher head0.339
Teacher spread0.185 · 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.

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

Citations14
Published2008
Admission routes3
Has abstractyes

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