Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
Bibliographic record
Abstract
This volume contains 40 regular papers and 44 brief announcements selected for the 27th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, held on August 18-21 in Toronto, Ontario, Canada. This volume also includes abstracts of keynotes by Joe Halpern, Don Towsley and Peter Druschel, as well as abstracts of talks delivered in a mini symposium honoring Nancy Lynch's 60th birthday. The latter was organized in conjunction with CONCUR, which was co-located with PODC. 132 papers were submitted to the regular papers track, and 55 were submitted to the brief announcements track. The selection of papers for presentation was done by the program committee in a meeting that took place in Columbia University in April 10-11, following electronic discussions. Some papers that were not selected for full presentation were invited to be submitted as brief announcements. Though all submissions were carefully read and evaluated, the papers were not formally refereed. It is expected that many of these papers will appear in more complete and polished form in refereed scientific journals. In keeping with the tradition of previous years, a selection of papers has been invited to appear in a special issue of Distributed Computing dedicated to PODC 2008.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.069 | 0.054 |
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".