Introduction to Special Issue on: Mobile Applications
Bibliographic record
Abstract
Preamble: This is the second special issue of the International Journal of Computing and Digital Systems (IJCDS) that focuses on mobile applications, and publishes selected papers from the 2nd International Workshop on Mobile Applications (MobiApps 2015), 24-26 August 2015, Rome, Italy.MobiApps 2015 was held in conjunction with the 12th International Conference on Mobile Web and Intelligent Information Systems (MobiWIS 2015), and the 3rd International Conference on Future Internet of Things and Cloud (FiCloud 2015) [1,2].The first special issue on mobile applications has been published in July, 2015 [3,4].In the MobiApps 2015 workshop the technical committee accepted 7 papers from all submitted papers covering a wide range of topics related to the theme of mobile applications and mobile technologies [5][6][7][8][9][10][11]. From these papers, 4 have been selected to be submitted as extended versions in the special issue on mobile applications in the IJCDS journal [5][6][7][8], in addition to another four papers selected from normal submission to the IJCDS journal [12][13][14][15].
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.138 | 0.101 |
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".