A Fat Client OS Architecture Supported by Semi-network Resources
Why this work is in the frame
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Bibliographic record
Abstract
Purpose–This study is to explore a way toretainthe strengths and eliminatethe weaknesses of the existingarchitecture oflocal OS and cloud OS,then create an innovativeone, which is referredto as semi-network OS architecture.Method–The elements of semi-network OS architecture includes networkresources, localresources, and semi-mobile hardware resources; among them, networkresources are the expanded portionof OS, which is used to ensure the scalability of OS; local resources are the base portion of OS, which is used to ensure the stability of local computing, as well as the autonomy of user operations; the semi-mobile hardware resource is OSPU, which is used to ensure the positioning and security of dataflow.Results–Thefat client OS relies on the network shared resources,local exclusive resources,and semi-mobilehardware resources (OSPU), not relies solely on a single resource, to perform its tasks on a fat client, in thisarchitecture, most of the system files of OS on a fat client isderived from OS server, which is a network shared resources, and the rest of system files of OS is derived from OSPUof a fat client, which is a non-network resource, so the architecture of OShas "semi-network" attribute, wherein the OSPU is a key subordinate component for data processing and security verification,the OS server is a storage place rather than operating a placeof system files, and system files that stored on a server can only be downloaded to a fat client to carry out their mission.Conclusion–A complete OS is divided into base portion and expanded portion, and this "portion" division of OS enables a fat client to be dually supported by remote network resources and local non-network resources, therefore, it is expected to make a fat client more flexible, safer and more reliable, and more convenient to be operated.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it