Fusion in coset CFT from admissible singular-vector decoupling
Bibliographic record
Abstract
: Fusion rules for Wess-Zumino-Witten (WZW) models at fractional level can be dened in two ways, with distinct results. The Verlinde formula yields fusion coecients that can be negative. These signs cancel in coset fusion rules, however. On the other hand, the fusion coecients calculated from decoupling of singular vectors are non-negative. They produce incorrect coset fusion rules, however, when factorisation is assumed. Here we give two prescriptions that yield the correct coset fusion rules from those found for the WZW models by the decoupling method. We restrict to the Virasoro minimal models for simplicity, and because decoupling results are only complete in the c su(2) case. PACS: 11.25Hf Keywords: conformal eld theory, nonunitary cosets, fusion, singular vectors hep-th/0007088 1 pmathieu@phy.ulaval.ca; work supported by NSERC (Canada) and FCAR (Quebec). 2 rasmussj@cs.uleth.ca; work supported by a PIMS Postdoctoral Fellowship and by NSERC (Canada). 3 walton@uleth.ca; work...
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".