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Record W2033423182 · doi:10.1029/2006gl026050

Subsurface temperatures during the last millennium: Model and observation

2006· article· en· W2033423182 on OpenAlexafffundabout
Hugo Beltrami, J. Fidel González‐Rouco, M. B. Stevens

Bibliographic record

VenueGeophysical Research Letters · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsSt. Francis Xavier University
FundersNatural Sciences and Engineering Research Council of CanadaMinisterio de Economía y CompetitividadSt. Francis Xavier University
KeywordsForcing (mathematics)ClimatologyBoreholeGCM transcription factorsGeneral Circulation ModelNatural (archaeology)GeologyEnvironmental scienceClimate changeClimate modelAtmospheric sciencesOceanography

Abstract

fetched live from OpenAlex

General Circulation Models (GCMs) used to distinguish anthropogenic forcing of the Earth's past climate from its natural variability need to be validated by observations. The GCM ECHO‐g was used to produce three millennial simulations of the Earth's climate. Two simulations include changes in anthropogenic and natural external forcing factors through the last millennium, differing only in their initial conditions, and a control run with constant external forcing representing internal variability. Since the ground contains a record of long‐term trends in SAT, we use borehole temperatures in Canada, grouped into regions, as a record of past climate. The regional average SATs from ECHO‐g were used to solve the forward subsurface thermal profile, and compared with the underground temperature anomalies observed at each region. In all cases simulated subsurface anomalies from the forced simulations are in better agreement with observations than those from the control simulation.

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 score0.075
Threshold uncertainty score0.149

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.030
GPT teacher head0.266
Teacher spread0.237 · 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

Citations27
Published2006
Admission routes3
Has abstractyes

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