The experience and suggestion about the security management of foreign oil fields
Bibliographic record
Abstract
The Canadian government has established a sound energy management agencies and possesses well developed oil and gas safety regulations and standards system.some methods as follow are carried out for the Safety supervision department inspecting the oil and gas companies:setting priorities supervision to some important enterprises;making sure the good quality of law enforcement personnel for professional supervision and inspection;increasing the punishment to promote the safety management levels.The Oil and gas enterprise security management and operator training is undertaken by professional training institutions,in particular they emphasis on training in well control and anti-hydrogen sulfide,all front-line operational staffs have to receive ENFORM training to make their work qualification approved by the companies and the security environment supervision department.according to author's experience from the development of Canadian oil field security management and combining with the domestic reality,some suggestion is put forward here :first,we should establish integrated safety database management system for enterprise security to provide the basis for production management ;second,we should improve the accident reporting system;and also improve enterprise security and cultural levels,and create a good safety work environment;at last,contractor safety management should be enhanced,and build a harmonious and safe environment for both sides.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".