Bibliographic record
Abstract
Purpose–This research aims to supplement or replace the existing embedded OS with architecture of semi-network OS, so as to form a co-mobile computing system and to create a new type of networked mobile devices.Method–Co-mobile computing system can only be realized by combining network resources with local resources, then this combination of resources is used to promote intelligent mobile devices to integrate into modern network environment more quickly and comprehensively, and then intelligent mobile devices become real networked one.Result–Under co-mobile computing system, networked mobile devices can not only safely use local resources to maintain the stability and reliability of devices operation, which is mainly attributed to the base portion of semi-network OS(similar to existing embedded OS), but also make full use of network resources to remedy the defect of existing embedded OS,which is mainly attributed to the expanded portion of semi-network OS. Conclusion–The concept of co-mobile computing mentioned here is defined as a data computing system specially designed for networked mobile devices, which is based on semi-network resources and formed by the merging of multiple networked mobile devices with different embedded OS and functions, now, the semi-network OS architecture will pave a new way for advancement of co-mobile computing system.The semi-network OS refers to that a complete operating system is divided into an expanded portion and a base portion,where the expanded portion of OS is stored on network server, which is ready to be downloaded at any time (similar to a supplement to embedded OS), and the base portion of OS is stored in OSPU located on local platform(similar to embedded OS); wherein the OSPU is a key hardware component in semi-network OS architecture and is movably installed on different local platforms by users.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".