Bibliographic record
Abstract
The ever increasing market penetration of smart-phones, tablets, and netbooks, along with the ubiquitous availability of wireless networks are deeply influencing the way people live, work, interact, and socialize. However, the broad popularity and diffusion of innovative services and applications tailored at mobile users is also raising challenging research issues that require us to rethink available mobile technology solutions to meet the emerging needs of a broader and ever growing user base. The goal of MoWNet is to bring together researchers and scientists to present and discuss advances on selected topics in Mobile & Wireless Networking. The conference aims to address recent research results and to present their methodologies, models, technologies, systems, tools, applications, work in progress and experiences. Following MoWNet'2016, MoWNET'2014, MoWNet'2013, iCOST'2012 and iCOST'2011 which were held in Cairo/Egypt, Rome/Italy, Montreal/Canada, Avignon/France and Shanghai/China, respectively, MoWNet'2017 will be held in Avignon, France, during May 17-19, 2017. The MoWNet'2017 technical program will deliver high quality technical papers that have been reviewed and selected by an international program committee.
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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.088 | 0.070 |
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".