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Record W2103584109 · doi:10.7202/032684ar

The Effects of the Laurentide Ice Sheet on North American Climate during the Last Glacial Maximum

2008· article· en· W2103584109 on OpenAlexvenueno aff
Anthony J. Broccoli, Syukuro Manabe

Bibliographic record

VenueGéographie physique et Quaternaire · 2008
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsIce sheetLast Glacial MaximumGeologyClimatologyIce-sheet modelGlacial periodIce streamPrecipitationPaleoclimatologyClimate modelClimate changeOceanographyCryosphereSea iceGeomorphologyGeographyMeteorology

Abstract

fetched live from OpenAlex

A climate model, consisting of an atmospheric general circulation model coupled with a simple model of the oceanic mixed layer, is used to investigate the effects of the continental ice distribution of the last glacial maximum (LGM) on North American climate. This model has previously been used to simulate the LGM climate, producing temperature changes reasonably in agreement with paleoclimatic data. The LGM distribution of continental ice according to the maximum reconstruction of HUGHES et al. (1981) is used as input to the model. In response to the incorporation of the expanded continental ice of the LGM, the model produces major changes in the climate of North America. The ice sheet exerts an orographic effect on the tropospheric flow, resulting in a splitting of the midlatitude westerlies in all seasons but summer. Winter temperatures are greatly reduced over a wide region south of the Laurentide ice sheet, although summer cooling is less extensive. An area of reduced soil moisture develops in the interior of North America just south of the ice margin. At the same time, precipitation increases in a belt extending from the extreme southeastern portion of the ice sheet eastward into the North Atlantic. Some of these findings are similar to paleoclimatic inferences based on geological evidence.

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 score0.752
Threshold uncertainty score0.993

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 studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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

Citations48
Published2008
Admission routes1
Has abstractyes

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