Proceedings of the twenty-seventh ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Bibliographic record
Abstract
This volume contains the proceedings of the Twenty-Seventh ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS 2008), held in Vancouver, Canada, on June 9-11, 2008, in conjunction with the 2008 ACM SIGMOD International Conference on Management of Data. The proceedings include one paper based on the keynote address by Peter Buneman, two papers based on the invited tutorials by Mikolaj Bojanczyk and Wenfei Fan, and 28 contributed papers that were selected by the Program Committee from 159 submissions. Most of these papers are preliminary reports on work in progress. While they have been read by program committee members, they have not been formally refereed. Many of them will probably appear in more polished and detailed form in scientific journals. The program committee selected the paper Estimating PageRank on Graph Streams by Atish Das Sarma, Sreenivas Gollapudi and Rina Panigrahy for the PODS 2008 Best Paper Award, and the paper Evaluating Rank Joins with Optimal Cost by Karl Schnaitter and Neoklis Polyzotis for the PODS 2008 Best Newcomer Award. Warmest congratulations to the authors of these papers. I thank all authors who submitted papers to the symposium, and the members of the program committee for the enormous amount of work they have done. The program committee did not meet in person, but carried out extensive discussions during the electronic PC meeting.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.087 | 0.060 |
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".