Bibliographic record
Abstract
The number of mobile phone users has grown surpassing the PC users due to its mobility and handiness, increasing of its processing power and memory, continuous growth of its applications and operating system's capabilities.Calendar is one of the common applications that are made available to these devices.Users are more dependent in using calendar to plan their daily activities.In a long term, the aggregation of these accounts can be the source of information in analyzing an individual's favorites, routines events, social contacts or personal beliefs.We developed a system to deliver the Mobile News Content (MNC) to individuals based on the information extracted from the calendar.The key capability of MNC is the ability to deliver news relevant to the current context of the individual with respect to the event, social and time.We propose an integrated suite that consists of event-capturing engine, news content controller and context calendar to enhance the news delivery capabilities.The event-capturing engine senses the actual activity that takes place and synchronizes with the context calendar automatically.The news content controller manages the delivery of news based on the event being sensed and the planned activity extracted from the context calendar.This paper discusses the components of the suite, system architecture and the system workflow of the suite.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".