Bibliographic record
Abstract
We explore the notion of generalization in the setting of symbolic mathematical computing. By "generalization" we mean the process of taking a number of instances of mathematical expressions and producing new expressions that may be specialized to all the instances. We identify a number of ways in which generalization may be useful in the setting of computer algebra. We formalize this generalization as an antiunification problem.The process of antiunification is the dual of unification. It takes two expressions E 1 , E 2 ∈ E (Σ, V ) and produces E 3 ∈ E (Σ, V ) such that there exist substitutions σ 1 and σ 2 such that σ 1 ( E 3 ) = E 1 and σ 2 ( E 3 ) = E 2 . We call the pair of substitutions an antiunifier and the resulting expression a generalization of the expressions. An antiunifier always exists, but is not necessarily unique. There is, however, a unique most specific antiunifier that places the most restrictions on the variables. This gives the most specific generalization , which is unique up to renaming of variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.004 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.004 |
| 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".