Bibliographic record
Abstract
U (TP)2 is a theorem prover developed to support the Unifying\nTheories of Programming (UTP) framework. Its primary design\ngoal was to support the higher-order logic, alphabets, equational reasoning\nand programs as predicates" style that is prevalent in much of\nthe UTP literature, from the seminal work by Hoare & He onwards.\nIn this paper we focus on the underlying logic of the prover, emphasising\nthose aspects that are tailored to support the style of proof so\noften used for UTP foundational work. These aspects include support\nfor alphabets, type-inferencing, explicit substitution notation, and explicit\nmeta-notation for general variable-binding lists in quanti ers. The\nneed for these features is illustrated by a running example that develops\na theory of UTP designs. We nish with a discussion of issues regarding\nthe soundness of the proof tool, and linkages to existing industrial\nstrength" provers such as Isabelle, PVS or CoQ.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".