Bibliographic record
Abstract
Social networking services can be broadly defined as internet-or mobile-based social spaces designed to facilitate communication, collaboration, and content sharing across networks of contacts. Services of social networks attract clients and try to cover all their needs. Every internet user has a group of social accounts according to his/her needs for example in Facebook, Skype, Twitter or others. But the problem is how client can manage a group of accounts? Iterative checking of every account is done because the services are independent. We will introduce in this article a new approach to achieve social network aggregation that deals with client as one class has many attribute (accounts). Also we will give an example as an application (called LU) to combine all these services with less use of computer or phone CPU. A new account will be implemented on the middle server between the social services server and the client. This account consists of ontology that combines all traits of social services (profile, friends, etc) and we will introduce a social SOA (SSOA) that will manage the new social service. This way will decrease the use of computer or phone CPU because clients will not have independent updates for his social events and only one account will be used. Also server will send update about all accounts in just one message.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.008 |
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".