Proceedings of the 28th ACM symposium on Principles of distributed computing
Bibliographic record
Abstract
This volume contains 27 regular papers and 36 brief announcements selected for the 28th ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, held on August 10-12 2009 in Calgary, Alberta, Canada. The contributed regular papers are selected from 110 submissions. The brief announcements are selected from 57 submissions, 31 of them fresh and 21 original submissions not accepted as regular contributions but encouraged to resubmit as brief announcements. This volume also includes abstracts of keynote addresses by Sarita V. Adve, Bruce A. Hendrickson, and Robbert van Renesse, as well as abstracts for papers from Yahoo! Research, Facebook, and Google presented in an invited session on industrial applications of distributed algorithms. The industrial session and the first two keynotes were organized in collaboration with SPAA'09, which this year is co-located with PODC. After a week of preliminary electronic discussions, the regular papers were selected during a physical program committee meeting on April 2nd in Austin, Texas. The meeting was attended by 26 of the 31 committee members, with the remaining five members connected by phone. Every submission was carefully read and evaluated by at least 3 PC members. 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 2009.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.061 | 0.045 |
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".