Автоматизированная система учета и анализа несчастных случаев на энергоустановках
Bibliographic record
Abstract
In 1999 on the basis of «Mosgosenergonadzor» there have been begun works on working out of information-analytical system (ИАС) bodies of Gosenergonadzor «Power efficiency», consisting of a number of the functional subsystems one of which is the subsystem «The Account and the analysis of accidents on power installations». The instructions of Department of the State power supervision and power savings №32-01-04/38 from 29.11.2000 to Mosgosenergonadzor had been assigned responsibilities under the account and the analysis of accidents on thermal and electric installations in the Russian Federation. For maintenance of introduction of a subsystem of the automated account and the analysis of accidents of Management of Gosenergonadzor in subjects of the Russian Federation organised the tax and submission of the information on each accident in which investigation the inspector of Power supervision, составлющий the accident Registration form participated. The registration form was filled in with the inspector of Gosenergonadzor during realisation of investigation of accident according to applied Methodical recommendations and stored in investigation materials. Since January, 1st, 2001, before commissioning IAC, copies of Cards were monthly directed by e-mail to corresponding regional governments of Gosenergonadzor to 20 dates, following accounting month. Regional governments of Gosenergonadzor of a copy of cards quarterly directed in Mosgosenergonadzor by e-mail to 30 dates, following accounting quarter, since January, 1st, 2001.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.019 |
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".