Realistic lattice determination of<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>α</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>M</mml:mi><mml:mi>Z</mml:mi></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:math>revisited
Bibliographic record
Abstract
We revisit the earlier determination of ${\ensuremath{\alpha}}_{s}({M}_{Z})$ via perturbative analyses of short-distance-sensitive lattice observables, incorporating new lattice data and performing a modified version of the original analysis. We focus on two high-intrinsic-scale observables, $\mathrm{log}({W}_{11})$ and $\mathrm{log}({W}_{12})$, and one lower-intrinsic-scale observable, $\mathrm{log}({W}_{12}/{u}_{0}^{6})$, finding improved consistency among the values extracted using the different observables and a final result, ${\ensuremath{\alpha}}_{s}({M}_{Z})=0.1192\ifmmode\pm\else\textpm\fi{}0.0011$, $\ensuremath{\sim}2\ensuremath{\sigma}$ higher than the earlier result, in excellent agreement with recent nonlattice determinations and, in addition, in good agreement with the results of a similar, but not identical, reanalysis by the HPQCD Collaboration. A discussion of the relation between the two reanalyses is given, focusing on the complementary aspects of the two approaches.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".