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Record W2038242343 · doi:10.1103/physrevlett.99.107001

Single-Gap<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mi>s</mml:mi></mml:math>-Wave Superconductivity near the Charge-Density-Wave Quantum Critical Point in<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>Cu</mml:mi><mml:mi>x</mml:mi></mml:msub><mml:msub><mml:mi>TiSe</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math>

2007· article· lv· W2038242343 on OpenAlexaff
S. Y. Li, Gang Wu, Xianhui Chen, Louis Taillefer

Bibliographic record

VenuePhysical Review Letters · 2007
Typearticle
Languagelv
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsCanadian Institute for Advanced ResearchUniversité de Sherbrooke
Fundersnot available
KeywordsSuperconductivityCondensed matter physicsPhysicsFermi surfaceCharge (physics)Charge density waveFermi levelElectrical resistivity and conductivityResidual resistivityQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

The in-plane thermal conductivity kappa of the layered superconductor CuxTiSe2 was measured down to temperatures as low as Tc/40, at x=0.06 near where the charge-density-wave order vanishes. The absence of a residual linear term at T-->0 is strong evidence for conventional s-wave superconductivity in this system. This is further supported by the slow magnetic field dependence, also consistent with a single gap, of uniform magnitude across the Fermi surface. Comparison with the closely related material NbSe2, where the superconducting gap is 3 times larger on the Nb 4d band than on the Se 4p band, suggests that in Cu0.06TiSe2 the Se 4p band is below the Fermi level and Cu doping into the Ti 3d band is responsible for the superconductivity.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.128

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0380.006

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 head0.037
GPT teacher head0.280
Teacher spread0.243 · 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 designBench or experimental
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

Citations70
Published2007
Admission routes1
Has abstractyes

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