Programming infinite structures using copatterns
Bibliographic record
Abstract
Infinite structures are an integral part of computer science as they serve as representations for concepts such as constantly running devices and processes or data communication streams.Due to their importance, it is crucial that programming languages are equipped with adequate means to encode and reason about infinite structures.This thesis investigates the recent idea of copatterns, a device to represent infinite structures in a fashion dual to usual definitions of finite data, by integrating it to Levy's Call-by-Push Value language.We define a coverage algorithm for copattern matching definitions.We prove that evaluation preserves types and that well-typed terms do not get stuck.We define a translation from our language to Levy's and prove that this translation preserves evaluation. RésuméLes structures infinies font partie intégrante de l'informatique puisqu'elles permettent la représentation de concepts tels que des processus ou appareils à exécution continue ou de flux de données servant à la communication entre différents appareils.En raison de leur importance, il est crucial que les langages de programmation soient capables d'adéquatement définir les structures infinies.Ce mémoire étudie le concept de comotifs qui permettent de représenter les structures infinies d'une façon duale aux définitions usuelles de données finies.Ce concept est intégré dans une extension du langage d'appel par valeur empilée de Levy.Nous présentons un algorithme de couverture pour les définitions de filtrage par comotif.Nous faisons la démonstration que les séquences d'évaluation préservent les types et que les expressions adéquatement typées ne bloquent pas.Nous présentons aussi une traduction de notre langage vers celui originellement conçu par Levy et nous démontrons que cette traduction s'harmonise avec l'évaluation des deux langages.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".