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Record W2156214799 · doi:10.1029/2001gl014310

Continental heat gain in the global climate system

2002· article· en· W2156214799 on OpenAlexaff
Hugo Beltrami, Jason E. Smerdon, Henry N. Pollack, Shaopeng Huang

Bibliographic record

VenueGeophysical Research Letters · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsSt. Francis Xavier University
Fundersnot available
KeywordsCryosphereLithosphereAtmosphere (unit)Heat fluxGeologyClimatologyClimate modelAtmospheric sciencesEnvironmental scienceGlobal warmingFlux (metallurgy)Climate changeEarth scienceGeophysicsHeat transferMeteorologySea iceOceanographyTectonicsGeographySeismologyMaterials science

Abstract

fetched live from OpenAlex

Recent estimates have shown the heat gained by the ocean, atmosphere, and cryosphere as 18.2 · 1022 J, 6.6 · 1021 J, and 8.1 · 1021 J, respectively over the past half‐century. However, the heat gain of the lithosphere via a heat flux across the solid surface of the continents (29% of the Earth's surface) has not been addressed. Here we calculate that component of Earth's changing energy budget, using ground‐surface temperature reconstructions for the continents. In the last half‐century there was an average flux of 39.1 mW m−2 across the land surface into the subsurface, leading to 9.1 · 1021 J absorbed by the ground. The heat inputs during the last half‐century into all the major components of the climate system — atmosphere, ocean, cryosphere, lithosphere‐reinforce the conclusion that the warming during the interval has been global.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.027

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.041
GPT teacher head0.292
Teacher spread0.252 · 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

Citations122
Published2002
Admission routes1
Has abstractyes

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