Proceedings of the first workshop on Urban networking
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2012 Workshop on Urban Networking (UrbaNe). UrbaNe aims at promoting the discussion on networking solutions for upcoming smart/digital cities, by fostering the debate among participants on (i) the identification of the urban features that are the most critical to networking, (ii) the definition and the classification of networking challenges deriving from such features, and (iii) the proposal of original network solutions that can cope with these challenges. UrbaNe 2012 received 19 submissions from 10 countries in Africa, Asia, Europe, Northern and Southern America. The reviewing process led to acceptance of 8 papers that cover a variety of topics, including urban networking challenges, citywide measurements, and management of energy and mobility in metropolitan areas. In addition, the program features invited talks by renowned speakers such as Daniel Kofman (Telecom ParisTech and RAD Data Communication) and Daniele Quercia (University of Cambridge).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.092 | 0.024 |
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".