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Record W4221050910 · doi:10.5194/egusphere-egu22-1838

Arctic Warming: A Perspective from the Underground

2022· preprint· en· W4221050910 on OpenAlexaff
Francisco José Cuesta‐Valero, Hugo Beltrami, Almudena García‐García, Fernando Jaume-Santero, Stephan Gruber

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsCarleton UniversitySt. Francis Xavier University
Fundersnot available
KeywordsPermafrostArcticContext (archaeology)Environmental scienceSubsurface flowClimatologyThe arcticLatent heatGlobal warmingClimate changeSoil waterAtmospheric sciencesGroundwaterHydrology (agriculture)OceanographyGeologySoil scienceMeteorologyGeographyGeotechnical engineering

Abstract

fetched live from OpenAlex

The thermal regime of the Arctic subsurface is important, for example, in the context of greenhouse-gas release from thawing permafrost soils. Measurements of Arctic subsurface temperatures, however, are scarce and limited in time, with virtually no observations over climatological time scales. We address this gap in knowledge by estimating the long-term evolution of subsurface temperatures in the Arctic (north of 60ºN) since 1600 Common Era (CE) to the present using 110 deep subsurface temperature profiles. The Arctic subsurface has warmed by 1.7±0.8 ºC during 1970-2000 CE. These estimates are conservative, as the effects of latent heat are not included in the analysis. Although there are significant spatial variations, the Arctic subsurface is warming faster than the global land surface and subsurface (1.2±0.2 ºC) during the same period. Uncertainties in this analysis arise mostly from deficient knowledge about the subsurface physical properties and limited data coverage.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.051
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.075
GPT teacher head0.278
Teacher spread0.203 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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