Cycle Symmetry Reduction and Its Extension on Three-Valued Models
Bibliographic record
Abstract
为了将对称化简扩展到更多的非对称系统上,扩展了传统的基于自同构的对称性,提出了一种称为循环对称的新的对称性.证明了采用循环对称置换群或者由一组循环对称置换所生成的置换群仍可得到与原模型互模拟的对称商结构,从而达到化简系统规模的目的.进一步地,研究如何将对称化简应用于多值模型.多值模型可以有效地表示系统中的不确定信息,正越来越多地用于软件系统的建模与分析中.针对一种具体的多值模型——三值模型,定义传统的对称化简和循环对称化简在其上面的扩展.最后,分析三值模型的商结构与由约简得到的二值模型商结构之间的关系,证明了两种途径的等价性.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".