Bibliographic record
Abstract
Akin's notion of good measure, introduced to classify measures on Cantor sets, has been translated to dimension groups and traces by Bezuglyi and the author, but emphasizing the simple (minimal dynamical system) case.Here we permit nonsimplicity.Goodness of tensor products of large classes of non-good traces (measures) is established.We also determine the pure faithful good traces on the dimension groups associated to xerox-type actions on AF C*-algebras; the criteria turn out to involve algebraic geometry and number theory.We also deal with a coproduct of dimension groups, wherein, despite expectations, goodness of direct sums is nontrivial.In addition, we verify a conjecture of Bezuglyi and Handelman (2014) concerning good subsets of Choquet simplices, in the finite-dimensional case.Introduction and definitions 365 1. Characterization of goodness 368 2. Tensor products 373 3. Examples from xerox actions of tori on UHF algebras 379 4. Direct sums and goodness 389 5. Good sets of traces 398 Appendix A. Connections with zero-dimensional topological dynamics 408 Appendix B. Order unit good traces on ޚ k 412 Appendix C. Good simplices
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".