Bibliographic record
Abstract
It is our great pleasure to welcome you to the Demo Track of WWW 2016, The 25th International World Wide Web Conference, held in Montreal, Canada, during April 11-15, 2016.The WWW 2016 Demo Track, like in the tradition of WWW Demo conference series, allows researchers and practitioners to demonstrate new systems in a dedicated session. Demo contributions are based on an implemented and tested system that pursues one or more innovative ideas in the interest areas of Web data and information management, Web search, Web intelligence tools, Web mining, social network applications and so forth. Topics of interest for the 2016 edition's conference include (but are not limited to) the following ones: Behavioral Analysis and PersonalizationBig Data on the WebCrowdsourcing Systems and Social MediaContent AnalysisGraph Data Management and MiningHigh-Performance Infrastructures for Data- Intensive Web TasksInternet Economics and MonetizationPervasive Web and MobilitySecurity and PrivacySemantic WebSocial Networks and Graph AnalysisWeb Information RetrievalWeb Infrastructure: Datacenters, Content Delivery Networks, and Cloud ComputingWeb MiningWeb ScienceWeb Search Systems and ApplicationsDemo contributions come from academic researchers, industrial practitioners with prototypes or inproduction deployments, as well as from any W3C-related activities. All have in common to show innovative use of Web-based techniques.The WWW 2016 Demo Track call for papers attracted 65 submissions from all over the world (USA, North America, South America, Europe, Australia, Asia, Africa). The program committee reviewed and accepted a very selected collection of 29 papers, and the final statistics is the following: WWW 2016 Demo Track Statistics Number of Submitted Papers 65 Number of Accepted Papers 29.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.479 | 0.210 |
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; the direct Gemma label and the distilled Codex classifier 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".