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Record W4416203157 · doi:10.1126/sciadv.adw9958

Robust increase in observed heat storage by the global subsurface

2025· article· en· W4416203157 on OpenAlexafffund
Francisco José Cuesta‐Valero, Almudena García‐García, Hugo Beltrami, Félix García‐Pereira, J. Fidel González‐Rouco, Jian Peng

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldEnergy
TopicGeothermal Energy Systems and Applications
Canadian institutionsSt. Francis Xavier University
FundersNextGenerationEUMinisterio para la Transición Ecológica y el Reto DemográficoNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlexander von Humboldt-Stiftung
KeywordsPermafrostBiogeochemical cycleSatelliteThermal energy storageEarth system scienceClimate changeGeothermal energyGlobal warmingScale (ratio)

Abstract

fetched live from OpenAlex

Changes in heat storage within the different components of the climate system alter physical and biogeochemical phenomena relevant for human societies and ecosystems. Among such processes, permafrost thawing, soil carbon storage, and surface energy exchanges depend on the persistent heat gain by the continental subsurface. Nevertheless, there are not enough data to estimate ground heat storage at the global scale after the year 2000. We solve this problem by expanding the database of geothermal data with remote sensing observations from satellite platforms. Estimates from satellite data show a heat gain between 16.4 ± 3.4 and 21.78 ± 0.62 zettajoules during the past six decades. The global ground heat storage presents a positive acceleration between 0.16 ± 0.15 and 0.624 ± 0.032 zettajoules per square decade, similarly to the rest of components of the Earth heat inventory. The planned satellite missions ensure the monitoring of the land component of the Earth heat inventory in the future.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.019
GPT teacher head0.261
Teacher spread0.242 · 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 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

Citations2
Published2025
Admission routes2
Has abstractyes

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