IPMU2024 Lisboa - Short Paper Proceedings
Bibliographic record
Abstract
This volume contains the Proceedings of the Short Papers of the 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024, held July 22-26, 2024, at the Centro de Congressos do Instituto Superior Técnico in Lisbon, Portugal. The conference was organized by INESC-ID and IDMEC of Instituto Superior Técnico, Universidade de Lisboa. The IPMU conference is organized every two years. It aims to bring together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems. Since 1986, the IPMU conference has provided a forum for exchanging ideas between theoreticians and practitioners working in these areas and related fields. For IPMU2024, the authors had the option of submitting either regular papers or shortpapers. The present volume contains 45 of the accepted short papers. Each of these papers was examined by the program chairs and selected reviewers for relevance and technical contribution, and contains a summary of a work that was presented and discussed orally during one of the IPMU2024 sessions.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.009 |
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; both teacher heads agree on what is shown here.
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".