Data Driven Research at LIS: the Laboratory of Information Systems at UNICAMP
Bibliographic record
Abstract
This article presents an overview of the research conducted at the Laboratory of Information Systems (LIS) at the Institute of Computing, UNICAMP. Its creation, in 1994, was motivated by the need to support data-driven research within multidisciplinary projects involving computer scientists and scientists from other fields. Throughout the years, it has housed projects in many domains - in agriculture, biodiversity, medicine, health, bioinformatics, urban planning, telecommunications, and sports - with scientific results in these fields and in Computer Science, with emphasis in data management, integrating research on databases, image processing, human-computer interfaces, software engineering and computer networks. The research produced 14 PhD theses, 70 MSc dissertations, 40+ journal papers and 200+ conference papers, having been assisted by over 80 undergraduate student scholarships. Several of these results were obtained through cooperation with many Brazilian universities and research centers, as well as groups in Canada, USA, France, Germany, the Netherlands and Portugal. The authors of this article are faculty at the Institute whose students developed their MSc or PhD research in the lab. For additional details, online systems, papers and reports, see http://www.lis.ic.unicamp.br and http://www.lis.ic.unicamp.br/publications
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.014 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.044 | 0.020 |
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".