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Record W2035714121 · doi:10.1016/j.yqres.2011.04.005

Long-term river discharge and multidecadal climate variability inferred from varved sediments, southwest Alaska

2011· article· en· W2035714121 on OpenAlexaff
Claire A. Kaufman, Scott F. Lamoureux, Darrell S. Kaufman

Bibliographic record

VenueQuaternary Research · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsQueen's University
FundersPreeclampsia Research LaboratoriesNorthern Arizona University
KeywordsVarvePacific decadal oscillationGeologyPrecipitationBayClimatologySedimentClimate changePeriod (music)OceanographyPhysical geographyHydrology (agriculture)Environmental scienceGeomorphologyEl Niño Southern OscillationGeographyMeteorology

Abstract

fetched live from OpenAlex

Abstract Sedimentological analyses of 289 years (AD 1718–2006) of varved sediment from Shadow Bay, southwest Alaska, were used to investigate hydroclimate variability during and prior to the instrumental period. Varve thicknesses relate most strongly to total annual discharge (r 2 = 0.75, n = 43, p < 0.0001). Maximum annual grain size depends most strongly on maximum spring daily discharge (r 2 = 0.63, n = 43, p < 0.0001) and maximum annual daily discharge (r 2 = 0.61, n = 43, p < 0.0001), while varve thickness is poorly correlated with maximum annual grain size (r 2 = 0.004, n = 287, p = 0.33). Relations between varve thickness and annual climate variables (temperature, precipitation, North Pacific (NP) and Pacific Decadal Oscillation (PDO) indices) are insignificant. On multidecadal timescales, however, regime shifts in varve thickness and total annual discharge coincide with shifts in NP and PDO indices. Periods with increased varve thickness and total annual discharge were associated with warm PDO phases and a strengthened Aleutian Low. The varve-inferred record of PDO suggests that any periodicity in the PDO varied over time, and that the early 19th century marked a transition to a more frequent or detectable shifts.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.088
GPT teacher head0.318
Teacher spread0.230 · 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 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

Citations21
Published2011
Admission routes1
Has abstractyes

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