Bibliographic record
Abstract
Number sentence morphing is an example of a type of edit puzzle. Edit puzzles choose a class of objects and a collection of editing operators. The goal of the puzzle is to change an initial configuration into a final one, using the edit operations, with the intermediate objects remaining within the selected class of of objects. Changing one English word into another by changing single letters, with all the intermediate steps also consisting of English words, is a typical example of an edit game. A number sentence is a well formed mathematical object, which can be used to generate a type of edit puzzle called Number sentence morphing. This puzzle has the player transform a false number sentence into a true one by removing and replacing symbols from a number sentence while leaving the sentence well formed. This type of puzzle is intended for teaching students the notion of well-formation in a game setting as well as providing practice with basic arithmetic. An evolutionary algorithm is used to construct number sentence morphing puzzles while certifying that they can be solved and requiring a minimal number of steps in a solution. The search for number sentences is template driven so particular forms of sentences are searched for in their own sets of runs. An unexpected outcome of this study is that the search landscapes for different templates are very different.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".