ARCHITECTURE OF THE INTELLECTUAL INFORMATION SYSTEM TO SUPPORT EXPERT DECISIONS ON STRATEGIC INNOVATIVE ENERGY DEVELOPMENT
Bibliographic record
Abstract
В статье рассмотрены методы построения интеллектуальной информационной системы для поддержки экспертных решений по стратегическому инновационному развитию энергетики. Обоснована необходимость применения методов анализа Больших данных (Big Data). Представлена архитектура интегрированного хранилища интеллектуальной информационной системы, основным компонентом которой является система онтологий, объединяющая данные и знания из различных источников. The article discusses methods of building an intelligent information system to support expert decisions on strategic innovative development of the energy sector. The necessity of using Big Data analysis methods has been substantiated. Architecture of an integrated repository of an intelligent information system is presented, in which the main component is a system of ontologies on the basis of which information, data and knowledge from various sources are combined.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".