Proceedings of the 5th MiNEMA Workshop (Middleware for Network Eccentric and Mobile Applications)
Bibliographic record
Abstract
Foreword On behalf of the organisation team, and the MiNEMA project, I would like to welcome you to the 5 th MiNEMA Workshop. MiNEMA is an European Science Foundation (ESF) Scientific Programme aiming to bring together European groups from different communities working on middleware for mobile environments. The programme intends to foster the definition and implementation of widely recognised middleware abstractions for new and emerging mobile applications. This year's MiNEMA workshop again promises to be extremely interesting, with 13 accepted contributions from 12 different countries: 10 within Europe (Switzerland, Portugal, UK, Denmark, Germany, Finland, Belgium, France, the Netherlands, and Italy), and two in North America (USA and Canada). The accepted papers cover a wide range of exciting topics, from Multicast Protocols and Overlay Networks through to Publish/Subscribe Systems and Wireless Sensor Applications. I would like to use this place to thank warmly the Programme Committee Members for their dedicated work, which allowed for a very smooth reviewing process. I would also like to thank the Organisation Team in Magdeburg for their perfect logistics, with a special mention to Thomas Kiebel for his tremendous support in all things technical, and in particular for setting up and maintaining the workshop web-site. Also thanks to Prof. Jörg Kaiser and Prof. Luís Rodrigues for their advise and help in arranging the workshop programme. Last but not least, thanks to the MiNEMA project and the European Science Foundation for providing the funding for this event. François Taïani 5 th MiNEMA Workshop Programme coordinator
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.059 | 0.064 |
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".