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Record W2736460921 · doi:10.1103/physrevb.96.125139

Phenomenological theories of the low-temperature pseudogap: Hall number, specific heat, and Seebeck coefficient

2017· article· en· W2736460921 on OpenAlexafffund
Simon Verret, Olivier Simard, M. Charlebois, David Sénéchal, A.–M. S. Tremblay

Bibliographic record

VenuePhysical review. B./Physical review. B · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhysics of Superconductivity and Magnetism
Canadian institutionsUniversité de Sherbrooke
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of Canada
KeywordsPseudogapCondensed matter physicsVan Hove singularitySeebeck coefficientCupratePhysicsHall effectScatteringWork (physics)SuperconductivityThermoelectric effectElectronQuantum mechanicsMagnetic fieldFermi level

Abstract

fetched live from OpenAlex

An elegant way to probe the mysterious ``pseudogap'' state in cuprates is to suppress superconductivity with strong magnetic fields and work at low temperature. Then, impurity scattering dominates, which simplifies calculations. A number of theories thus successfully explain the sudden change in carrier number observed experimentally. To tell these theories apart, the work here provides new predictions. First, it shows how electron pockets cause strong signatures in the specific heat and Seebeck coefficient. Second, it addresses inconsistencies in scattering rate approximations for the Hall and the Seebeck effects. Third, it shows the influence of the Van Hove singularity on these quantities.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0020.006
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.016
GPT teacher head0.324
Teacher spread0.308 · 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 designTheoretical or conceptual
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

Citations30
Published2017
Admission routes2
Has abstractyes

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