Issues in PetroChina's management of pipeline failure data and corresponding solutions
Bibliographic record
Abstract
In PetroChina, great importance is attached to the analysis and experience sharing of serious accidents, but rarely to minor accidents or incidents. In this circumstance, it is not conducive to discover rules of such accidents/incidents and thereby to guarantee the intrinsic safety of oil and gas pipelines and other production facilities. By investigating the oil and gas pipeline failure databases available in China and abroad, this paper justifi es the significance of establishing and maintaining the databases, and reveals the issues in PetroChina's oil and gas pipeline failure database, such as absence of management standards and lack of system constraints and policy incentives. Considering the definitions and reporting processes of failure within failure databases in the United States, Canada, Europe and the UK and the practices of failure information collection in pipeline industry in China, this paper also recommends to further promote PetroChina's oil and gas pipeline failure database.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".