Determinations of Vus using inclusive hadronic τ decay data
Bibliographic record
Abstract
Two methods for determining [Formula: see text] employing inclusive hadronic [Formula: see text] decay data are discussed. The first is the conventional flavor-breaking sum rule determination whose usual implementation produces results [Formula: see text] low compared to three-family unitary expectations. The second is a novel approach combining experimental strange hadronic [Formula: see text] distributions with lattice light-strange current–current two-point function data. Preliminary explorations of the latter show the method promises [Formula: see text] determinations competitive with those from [Formula: see text] and [Formula: see text]. For the former, systematic issues in the conventional implementation are investigated. Unphysical dependences of [Formula: see text] on the choice of sum rule weight, [Formula: see text], and upper limit, [Formula: see text], of the weighted experimental spectral integrals are observed, the source of these problems identified and a new implementation which overcomes these problems developed. Lattice results are shown to provide a tool for quantitatively assessing truncation uncertainties for the slowly converging [Formula: see text] OPE series. The results for [Formula: see text] from this new implementation are shown to be free of unphysical [Formula: see text]- and [Formula: see text]-dependences, and [Formula: see text] higher than those produced by the conventional implementation. With preliminary new [Formula: see text] branching fraction results as input, we find [Formula: see text] in excellent agreement with that obtained from [Formula: see text], and compatible within errors with expectations from three-family unitarity.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".