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Record W3032602544 · doi:10.1029/2020gl087942

Ice‐Wedge Evidence of Holocene Winter Warming in the Canadian Arctic

2020· article· en· W3032602544 on OpenAlexafffundabout
K. M. Holland, Trevor J. Porter, Duane Froese, Steven V. Kokelj, Casey Buchanan

Bibliographic record

VenueGeophysical Research Letters · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsGovernment of Northwest TerritoriesUniversity of AlbertaGeneral Electric (Canada)University of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaNatural Resources CanadaGeological Society of America
KeywordsArcticHoloceneClimatologyPaleoclimatologyArctic dipole anomalyArctic sea ice declineArctic ice packInsolationClimate changePrecipitationEnvironmental scienceArctic ecologyArctic geoengineeringOceanographySea icePhysical geographyGeologyGeographyDrift iceMeteorology

Abstract

fetched live from OpenAlex

Abstract Arctic summer temperatures mostly cooled over the last ~7 kyr, owing to decreasing summer insolation. However, knowledge of the winter season is limited in the Arctic paleoclimate literature. Here we develop a composite record of δ18O from ice wedges—a winter precipitation archive—to reconstruct changes in winter climate in the northwestern Canadian Arctic since ~7.4 kyr b2k. Our record shows a long‐term δ18O enrichment (+(0.14 ± 0.10)‰ kyr−1), suggesting winter temperatures increased since the mid‐Holocene, a finding that is corroborated by reconstructions from the Siberian Arctic. Winter warming over the last ~7 kyr is consistent with increasing winter insolation and greenhouse gas forcing. This study provides some of the first insights on the sensitivity of winter temperatures in the Canadian Arctic to past, and potentially future, climate forcings, and contributes to a more seasonally holistic understanding of the Arctic system.

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)0.000
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 score0.035
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.094
GPT teacher head0.325
Teacher spread0.231 · 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

Citations30
Published2020
Admission routes3
Has abstractyes

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