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Record W6910957989 · doi:10.5066/p9gvyfnw

Data release for Lake levels in a discontinuous permafrost landscape: Late Holocene variations inferred from sediment oxygen isotopes, Yukon Flats, Alaska

2018· dataset· en· W6910957989 on OpenAlexaboutno aff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2018
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsPermafrostRadiocarbon datingHoloceneAridPeriod (music)SedimentPaleoclimatologyIsotopes of oxygenLithology

Abstract

fetched live from OpenAlex

During recent decades, lake levels in the Yukon Flats region of interior Alaska have fluctuated dramatically. However, prior to recorded observations, no data are available to indicate if similar or more extreme variations occurred during past centuries and millennia. This study explores the history of Yukon Flats lake origins and lake levels for the past approximately 5,500 years from sediment analyses guided by previous work on permafrost extent, thermokarst, and modern isotope hydrology. Sediments dated by 210Pb and AMS radiocarbon indicate stable chronologies following initial lake initiation. Subsequent lithology is autochthonous, and oxygen isotope ratios of endogenic carbonate reflect lake level change at multiple time scales. Sediment results indicate high lake levels between approximately 4000 and 1850 cal yr BP, which is interpreted to reflect wetter-than-modern conditions. Lower lake levels with short-lived high stands during the past approximately 800 years reflect generally arid conditions with brief wet intervals similar to the regions moisture regime today. The millennial trend is one of increasing aridity and corresponds closely with fire reconstructions and regional paleoclimatic trends. We conclude that high-magnitude lake-level fluctuations and decadal scale trends occurred before the observational period and are persistent hydroclimatic features of the Yukon Flats region.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.039
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.005

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 head0.035
GPT teacher head0.263
Teacher spread0.228 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2018
Admission routes1
Has abstractyes

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