Bibliographic record
Abstract
The paper analyzed the problem of accessibility of content in other languages, it was found that many content may not be translated into the native language of users who want to access it, but at the same time there are many who want to help other users with this problem. The solution is a special information system that allows you to easily register and create your own translation, in which other users can participate, or join another already created one and help. As a result, the interested user can easily download the translation result and use it at his own discretion. The analysis of business processes for the creation and translation of the text was carried out. Based on this analysis, requirements for a future solution were developed. Business requirements were also identified. Among other things, a system use case model was developed and use case specifications were described. Lists with functional and non-functional requirements have also been developed. The functional model of the system was shown - algorithms: authorization, registration, password recovery, creating a new translation, generating a file with a new translation, generating a list of translations, managing users, viewing a translation, editing a translation text, checking the correctness of a translation, and moderating translations. A class diagram was developed, where you can see the main entities of the system and their relationships. A sequence diagram was also developed. The architecture of the information system was described. The system was implemented using the React.JS library and the Spring framework. The main processes of the system users were also described.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.027 |
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".