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Record W1978764969 · doi:10.1029/2008gl036125

Climate‐driven shifts in quantity and seasonality of river discharge over the past 1000 years from the hydrographic apex of North America

2008· article· en· W1978764969 on OpenAlexafffund
Brent B. Wolfe, Roland I. Hall, Thomas W. D. Edwards, Suzanne Jarvis, R. Niloshini Sinnatamby, Yi Yi, John W. Johnston

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of WaterlooWilfrid Laurier University
FundersBC HydroNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsGovernment of Ontario
KeywordsGlacierMeltwaterHydrographyClimate changeSnowSurface runoffPhysical geographyLittle ice ageGeologyRange (aeronautics)DischargeClimatologyPrecipitationStreamflowEnvironmental scienceOceanographyGeographyDrainage basinEcologyGeomorphology

Abstract

fetched live from OpenAlex

Runoff generated from high elevations is the primary source of freshwater for western North America, yet this critical resource is managed on the basis of short instrumental records that capture an insufficient range of climatic conditions. Here we probe the effects of climate change over the past ∼1000 years on river discharge in the upper Mackenzie River system based on paleoenvironmental information from the Peace‐Athabasca Delta. The delta landscape responds to hydroclimatic changes with marked variability, while Lake Athabasca level appears to directly monitor overall water availability. The latter fluctuated systematically over the past millennium, with the highest levels occurring in concert with maximum glacier extent during the Little Ice Age, and the lowest during the 11th century, prior to medieval glacier expansion. Recent climate‐driven hydrological change appears to be on a trajectory to even lower levels as high‐elevation snow and glacier meltwater contributions both continue to decline.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.038
Threshold uncertainty score0.954

Codex and Gemma teacher scores by category

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

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 head0.037
GPT teacher head0.267
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; 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

Citations50
Published2008
Admission routes2
Has abstractyes

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