-algebras of labelled graphs III—-theory computations
Bibliographic record
Abstract
In this paper we give a formula for the $K$ -theory of the $C^{\ast }$ -algebra of a weakly left-resolving labelled space. This is done by realizing the $C^{\ast }$ -algebra of a weakly left-resolving labelled space as the Cuntz–Pimsner algebra of a $C^{\ast }$ -correspondence. As a corollary, we obtain a gauge-invariant uniqueness theorem for the $C^{\ast }$ -algebra of any weakly left-resolving labelled space. In order to achieve this, we must modify the definition of the $C^{\ast }$ -algebra of a weakly left-resolving labelled space. We also establish strong connections between the various classes of $C^{\ast }$ -algebras that are associated with shift spaces and labelled graph algebras. Hence, by computing the $K$ -theory of a labelled graph algebra, we are providing a common framework for computing the $K$ -theory of graph algebras, ultragraph algebras, Exel–Laca algebras, Matsumoto algebras and the $C^{\ast }$ -algebras of Carlsen. We provide an inductive limit approach for computing the $K$ -groups of an important class of labelled graph algebras, and give examples.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".