Bibliographic record
Abstract
Researchers who make theoretical advances also need some way to demonstrate that an advance really does have general, overall positive consequences for system performance. For this it is necessary to evaluate the system on a set of problems that is sufficiently large and diverse to be somehow representative of the intended application area as a whole. It is only a small step from system evaluation to a communal system competition. The CADE ATP System Competition (CASC) has been run annually since 1996. Any competition is difficult to design and organize in the first instance, and to then run over the years. In order to obtain the full benefits of a competition, a thoroughly organized event, with an unambiguous and motivated design, is necessary. For some issues relevant to the CASC design, inevitable constraints have emerged. For other issues there have been several choices, and decisions have had to be made. This paper describes the evolution of CASC, paying particular attention to its design, design changes, and organization.
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 | 0.018 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 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".