Report on the 18 <sup>th</sup> British National Conference on Databases (BNCOD)
Bibliographic record
Abstract
The annual series of the British National Conference on Databases has been a forum for UK database practitioners and a focus for database research since 1981. In recent years, interest in this conference series has extended well beyond the UK.BNCOD 2001, the 18th conference in the series, was held at the CLRC Rutherford Appleton Laboratory (RAL) from 9th -11th July 2001. RAL hosts national large-scale facilities for advanced scientific research. The Information Technology Department collaborates with the Laboratory's data centres that manage terabytes of data in remote sensing, high-energy physics and astronomy.BNCOD 2001 included scientific papers, invited talks, a panel and a poster session. The BNCOD Programme Committee, chaired by Professor Carole Goble of Manchester University, selected for presentation at the meeting eleven papers, about one third of those papers submitted. Contributors were drawn from the Netherlands, Germany, Sweden, Canada and USA, as well as the UK. The audience of 60 attendees was chiefly drawn from the UK database community. The Proceedings are published by Springer-Verlag in the Lecture Notes in Computer Science series, and are available online at: http://link.springer.de/link/service/series/0558/tocs/t2097.htm.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.376 | 0.280 |
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".