ПОВЫШЕНИЕ ПОЖАРНОЙ БЕЗОПАСНОСТИ МАГИСТРАЛЬНЫХ НЕФТЕПРОВОДОВ ПУТЕМ СОВЕРШЕНСТВОВАНИЯ КАТОДНОЙ ЗАЩИТЫ
Bibliographic record
Abstract
Total length of pipeline systems of the Russian Federation makes more than 2 million km. Under the federal law № 116-FL by 21.07.1997 «About industrial safety of hazardous production facilities» underground main oil pipelines and petrochemical pipelines are dangerous production facilities. The most important problem of the energy complex is to ensure of reliability and safe operation of pipelines. Having poured out naphtha or oil product at the most adverse scenario of development of accident on the pipeline can lead to the fire that not only increases technological, ecological and economic damage, but also promotes emergence of threat to health and life of people. As the main indicator of danger of any production object serves the potential risk, that is the expected level of negative consequences of accidents in annual basis (the expected damage, probability of emergence of accidents with particular consequences, etc.). Ways of decrease in potential risk of main oil pipelines are indicated by statistical information on accident rate. Statistical data on failures of underground oil and petrochemical pipelines of Russia, the USA, Canada and the European Union countries indicates that one of the most significant causes of accidents leading to depressurization of pipelines with the leak of the transported product and fire is the corrosion of the outer surface of the pipe, that confirms a significance of anticorrosive protection in a problem of safety of operation of underground pipelines. In the same time there is no information in the Russian and foreign periodicals and monographs about the quantitative assessment of the impact of electrochemical protection and its elements to reducing the frequency of failures and fire risks.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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".