Theory of Computing 2010: Proceedings of the Sixteenth Computing: The Australasian Theory Symposium (CATS 2010), Brisbane, Australia, January 2010
Bibliographic record
Abstract
The 16th "Computing: The Australasian Theory Symposium - CATS" is an annual conference held in the Australia-New Zealand region, dedicated to theoretical computer science. CATS is part of the Australasian Computer Society Week (ACSW), an international annual conference event, supported by the Computing Research and Education Association (CORE) in Australia. ACSW 2010 is hosted by the School of Information Technology at the Queensland University of Technology (QUT) in Brisbane, Australia, January 18-21, 2010. CATS is an international, fully refereed conference publishing original research in all areas of theoretical computer science. The program committee in 2010 included members from Australia, New Zealand, USA, Canada, Japan, China and Hong Kong, UK, India, Switzerland, and Taiwan. In 2010 the conference received 28 submissions, of which 12 were accepted for publication, resulting in an acceptance rate of just below 43%. Each submission received three or four independent reviews from program committee members or sub-reviewers, and was discussed by the program committee. We would like to thank all the program committee members and sub-reviewers for their work, as well as all the authors for their contribution in making CATS a successful theory event.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.045 | 0.026 |
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".