Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Bibliographic record
Abstract
Welcome to SIGMOD 2008! We think you will find both the conference and the setting to be invigorating. natural timeless beauty of British Columbia will provide a fitting counterpoint to the dynamism of our field in which large scale, high performance, and ever more intelligent database systems are being conceived and deployed. This dynamism is reflected in our (extreme) keynote presentations, tutorials, research papers, demonstrations, industrial papers, and product presentations. only unfortunate side of our program is that the five parallel session structure may prevent you from hearing every talk in which you are interested. The conference statistics give an indication of how SIGMOD's selectivity. Out of 435 submitted research papers, we accepted 78; out of 40 submitted industrial papers, we accepted 15; out of 94 demo submissions, we accepted 30; and out of 15 tutorial submissions, we accepted 5. Reviewing is an imperfect art, so we may have rejected some papers that we should have accepted, but we hope the written reviews have helped authors improve their papers for future submissions. The main methodological innovation in SIGMOD this year has been the repeatability option. Papers submitting experiments were invited to submit code and data to enable the pioneering members of the repeatability committee to verify that the experiments worked as advertised. Any paper satisfying the repeatability criteria will include the sentence The results in this paper were verified by the SIGMOD repeatability committee. goal is to count our field among the repeatable sciences and to pave the way for the archiving of code and data. response to this initiative has been overwhelmingly positive and we look forward to a greater participation by all members of the community in the future.
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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.086 | 0.080 |
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".