Bibliographic record
Abstract
The Permutation Patterns 2007 conference was held 11–15 June 2007 at the University of St Andrews. This was the fifth Permutation Patterns conference; the previous conferences were held at Otago University (Dundein, New Zealand), Malaspina College (Vancouver Island, British Columbia), the University of Florida (Gainesville, Florida), and Reykjavík University (Reykjavík, Iceland). The organizing committee was comprised of Miklós Bóna, Lynn Hynd, Steve Linton, Nik Ruškuc, Einar Steingrímsson, Vincent Vatter, and Julian West. A half-day excursion was taken to Falls of Bruar, Blair Athol Castle and The Queen's View on Loch Tummel. There were two invited talks: Mike Atkinson (Otago University, Dunedin, New Zealand), “Simple permutations and wreath-closed pattern classes”. Martin Klazar (Charles University, Prague, Czech Republic), “Polynomial counting”. There were 35 participants, 23 talks, and a problem session (the problems from which are included at the end of these proceedings). All the main strands of research in permutation patterns were represented, and we hope this is reflected by the articles of these proceedings, especially the eight surveys at the beginning. The conference was supported by the EPSRC and Edinburgh Mathematical Society.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.441 | 0.283 |
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".