Bibliographic record
Abstract
We wish to answer the following question: what is the most appropriate language for describing the “contracts” that Maple routines offer? In this , we are seeking much more than types (which Maple does not have, at least statically), as th ese are not sufficiently expressive to capture what is going on. We also wish to study what is actually in Maple, rather than what should be there. Put another way, we do not expect to find that a type system like Aldor’s or Axiom’s would be especially helpful in expla ining Maple. Our real goal is a mathematical description of the interfaces between routines. As such, the only current terminology flexible enough to encompass reality is that of c ontracts, by which we mean simply statements of complex properties (static as well as dynamic) in a sufficiently general logic. This works focuses mainly on the requirements analysis phase of this project: we perform automated analyses of the complete Maple library, to understand the kinds of contracts that are in actual use. We wish to know which kinds of theorems would need to be proved in order to formally analyze the types, effects, invariants, a nd contracts present in the current code base. As this is a monumental task, we describe what knowledge we have currently been able to extract from a very systematic approach to the problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".