Tom llustrated on an implementation of the explicit rewriting calculus
Bibliographic record
Abstract
Following the experience of Elan, the Tom language was devel- oped to provide rewrite tools for implementation of calculi, for compilation and for XML-transformations. We will focus here on the former. Tom provides a language to define a syntax (a signature) embedded into Java. Then, we can perform pattern matching with support of associative matching modulo neutral element (also known as list-matching). Finally, we can guide the application of rules with a strategy language defining term traversals (namely evaluation/rewriting strategies). The originality of Tom is the combination of formal aspects with a general purpose language (such as Java). This combination leads to an agile language. At the same time, the strategy language inspired by Elan and Stratego gives the opportunity to reduce the code written in the general purpose language (and thus increase the formal parts). We will illustrate the presentation by an implementation of the explicit rewriting calculus, introduced at the last WRLA. This running example will demonstrate the adequacy of Tom for such a development, offered by the integration in a general purpose language and by the strategy language.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".