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Record W1973478069 · doi:10.1029/2006gl025753

A pan‐arctic evaluation of changes in river discharge during the latter half of the 20th century

2006· article· en· W1973478069 on OpenAlexaff
J. W. McClelland, Stephen J. Déry, Bruce J. Peterson, R. M. Holmes, Eric F. Wood

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Northern British Columbia
FundersDivision of Arctic SciencesNational Oceanic and Atmospheric AdministrationNational Science Foundation
KeywordsArcticEnvironmental scienceThe arcticGeologyDischargeOceanographyClimatologyPhysical geographyHydrology (agriculture)GeographyDrainage basinGeotechnical engineering

Abstract

fetched live from OpenAlex

Several recent publications have documented changes in river discharge from arctic and subarctic watersheds. Comparison of these findings, however, has been hampered by differences in time periods and methods of analysis. Here we compare changes in discharge from different regions of the pan‐arctic watershed using identical time periods and analytical methods. Discharge to the Arctic Ocean increased by 5.6 km 3 /y/y during 1964–2000, the net result of a large increase from Eurasia moderated by a small decrease from North America. In contrast, discharge to Hudson/James/Ungava Bays decreased by 2.5 km 3 /y/y during 1964–2000. While this evaluation identifies an overall increase in discharge (∼120 km 3 /y greater discharge at the end of the time period as compared to the beginning for Hudson/James/Unvaga Bays and the Arctic Ocean combined), the contrasting regional trends also highlight the need to understand the consequences of adding/removing freshwater from particular regions of the arctic and subarctic oceans.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.058
GPT teacher head0.302
Teacher spread0.244 · 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 source (direct Gemma or distilled Codex), 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

Citations389
Published2006
Admission routes1
Has abstractyes

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