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Record W2734613673 · doi:10.1002/2017gl073597

Thermal structure of the Kanto region, Japan

2017· article· en· W2734613673 on OpenAlexaff
Ikuko Wada, Jiangheng He

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsGeologySlabMantle wedgeSubductionForearcSeismologyMantle (geology)Pacific PlateVolcanic arcThermalGeophysicsTectonics

Abstract

fetched live from OpenAlex

Abstract Using a 3‐D numerical thermal model, we investigate the thermal structure of the Kanto region of Japan where two oceanic plates subduct. In a typical subduction setting with one subducting slab, the motion of the slab drives solid‐state mantle flow in the overlying mantle wedge, bringing in hot mantle from the back‐arc toward the forearc. Beneath Kanto, however, the presence of the subducting Philippine Sea plate between the overlying North American plate and the subducting Pacific plate prevents a typical mantle wedge flow pattern, resulting in a cooler condition. Further, frictional heating and the along‐margin variation in the maximum depth of slab‐mantle decoupling along the Pacific slab surface affect the thermal structure significantly. The model provides quantitative estimates of spatial variations in the temperature condition that are consistent with the observed surface heat flow pattern and distributions of interplate seismicity and arc volcanoes in Kanto.

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)0.000
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: Empirical
Teacher disagreement score0.108
Threshold uncertainty score0.215

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
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 head0.042
GPT teacher head0.288
Teacher spread0.245 · 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

Citations22
Published2017
Admission routes1
Has abstractyes

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