Unlocking the Hidden Power of the Mobile
Bibliographic record
Abstract
Today in the beginning of the 21st century, mobile devices are now ubiquitous. No matter where we go or what we do, we are touched by this new insatiable need for mobile computing. Mobile devices, especially mobile phones, have become the essential commodity item. In many countries the world over, mobile phone ownership is well above 100% market penetration. The main features predominantly used are text messaging and voice communications. The phones of today, however, have far more to offer than these interpersonal communication features. Many phones include components such as digital cameras, wireless data communication systems (Bluetooth), and music playback facilities. Some even include additional sensor technology such as accelerometers to detect motion. Java Virtual Machines (JVMs) are now shipped as standard with almost every phone that comes off the production line. This opens the door to a huge body of developers to create applications specifically directed to these small mobile computing devices. The area of mobile Java games is one area of growth, especially due to the ease of deployment. Mobiles are, however, capable of so much more. This chapter focuses on the computational abilities of these small portable computers. It provides a selection of concrete results that indicate that mobiles are more than capable of performing complex computational tasks; therefore, the future of computing is mobile.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".