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Record W2163409669 · doi:10.1175/2008jcli2342.1

Reconstructing Millennial-Scale, Regional Paleoclimates of Boreal Canada during the Holocene

2008· article· en· W2163409669 on OpenAlexafffundabout
André Viau, Konrad Gajewski

Bibliographic record

VenueJournal of Climate · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Ottawa
FundersCanadian Foundation for Climate and Atmospheric Sciences
KeywordsHoloceneClimatologyGeologyHolocene climatic optimumBorealPaleoclimatologyContext (archaeology)Climate changePrecipitationPhysical geographyOceanographyGeographyPaleontology

Abstract

fetched live from OpenAlex

Abstract Regional paleoclimate reconstructions for northern Canada quantify Holocene climate variability on orbital and millennial time scales and provide a context to better understand the current global warming. The reconstructions are based on available pollen diagrams from the boreal and low Arctic zones of Canada and use the modern analog technique (MAT). Four regional reconstructions document the space–time evolution of the climate during the Holocene. Highest summer and winter temperatures anomalies are found in central Canada during the early Holocene. Eastern Canada was relatively cool in the early Holocene, whereas central Canada was warmest at that time. Labrador was relatively dry in the early to mid-Holocene during which time western Canada was relatively moist. Millennial-scale temperature variations, especially the Medieval Warm Period and Little Ice Age are seen across the continent, with some suggestion of time-transgressive changes from west to east. At the millennial scale, precipitation anomalies are of opposite signs in eastern and western Canada. The results herein indicate that modern increases in temperatures in northern Canada far exceed natural millennial-scale climate variability.

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)0.001
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 score0.105
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.016
GPT teacher head0.220
Teacher spread0.205 · 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

Citations162
Published2008
Admission routes3
Has abstractyes

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