Bibliographic record
Abstract
The dependently typed lambda-calculus with algebraic datastructures is a programming language with very few primitives but a huge expressivity. The Coq proof assistant is built over one variant of this language, the CIC. Its semantics is extremely clear but it is verbose. Therefore, users do not write programs directly in CIC. Instead, Coq provides tools to elaborate programs incrementally using higher level constructions. Especially, mixing algebraic and dependent types increases the power and the difficulty of case analysis. Each case has a different type depending of the type of the constructor. Some cases are even impossible because of typing. These type casts and impossibility witnesses are explicit in CIC terms but they can be built mechanically. This thesis gives an algorithm to achieve this automation. As far as feedback from the system is concerned, interaction with human asks for a way to compute Coq programs without making their syntactical length explode. This thesis propose a new abstract machine designed for this purpose. Fixpoints provide a convenient way to deal with recursive datastructures. Nevertheless, ensuring their computation does not diverge on any entry is a challenging issue. It is tackled by the last chapter of this thesis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.010 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".