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Record W2116061942 · doi:10.1029/2009gl042239

Role of tropical Pacific SSTs in global medieval hydroclimate: A modeling study

2010· article· en· W2116061942 on OpenAlexaff
Robert Burgman, Richard Seager, Amy Clement, Celine Herweijer

Bibliographic record

VenueGeophysical Research Letters · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsClimatologyTropicsTropical AtlanticSea surface temperatureClimate modelGeologyTropical climateSouthern HemisphereOceanographyNorthern HemisphereEnvironmental scienceClimate changeGeography

Abstract

fetched live from OpenAlex

The role of tropical Pacific SSTs in driving global medieval hydroclimate is assessed. Using fossil coral records from Palmyra Atoll, tropical Pacific sea surface temperature (SST) boundary conditions are derived for the period 1320–1462 A.D. These boundary conditions consist of La Niña‐like mean state conditions in the tropical Pacific with inter‐annual and decadal variability about that altered state. The reconstructed SSTs in the tropical Pacific are used to force a 16 member ensemble of atmospheric general circulation model (AGCM) simulations, coupled to a one layer ocean model outside of the tropical Pacific. The AGCM simulations of medieval climate are compared with modern climate simulations for the period 1856–2005 A.D. and are shown to reproduce many aspects of medieval hydroclimate found in paleo‐proxy records for much of the Western Hemisphere, northern Eurasia, and the northern tropics. These results suggest that many features of global medieval hydroclimate changes can be explained by changes in tropical Pacific SSTs, though the potential role for other oceans is also discussed.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.027
GPT teacher head0.310
Teacher spread0.283 · 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 designSimulation or modeling
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

Citations34
Published2010
Admission routes1
Has abstractyes

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