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Joint extraction of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.gif" overflow="scroll"><mml:msub><mml:mi>m</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math> and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si2.gif" overflow="scroll"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi>u</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:math> from hadronic τ decays

2007· article· lv· W2345626666 on OpenAlexafffund
Kim Maltman, Carl E. Wolfe

Bibliographic record

VenuePhysics Letters B · 2007
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicParticle physics theoretical and experimental studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhysicsAlgorithmParticle physicsLattice (music)Machine learningMathematicsComputer science

Abstract

fetched live from OpenAlex

We study the simultaneous determination of ms and Vus from flavor-breaking hadronic τ decay sum rules using weights designed to bring under better control problems associated with the slow convergence of the relevant D=2 OPE series. Much improved stability and consistency is found as compared to the results of conventional analyses based on the “(k,0) spectral weights”. Results for ms are in excellent agreement with those of recent strange scalar and strange pseudoscalar sum rule analyses, as well as recent lattice analyses, while those for Vus agree within errors with the output from recent lattice-based Γ[Kμ2]/Γ[πμ2] and Kℓ3-based analyses. Very significant error reductions are shown to be expected, especially for Vus, once the improved strange spectral data from the B-factory experiments becomes available.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0020.004
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0010.002
Science and technology studies0.0040.004
Scholarly communication0.0030.003
Open science0.0030.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0880.005

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 head0.018
GPT teacher head0.244
Teacher spread0.226 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2007
Admission routes2
Has abstractyes

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