Bibliographic record
Abstract
Abstract Driving is one of the most hazardous tasks carried out by workers in the Western Canadian oil and gas industry. Significant distances are driven on a daily basis and drivers face numerous hazards during all seasons of the year. BP Canada Energy Company Canda Gas Business Unit implemented a multi-faceted approach to reducing driving incidents. The approach aimed to reduce the frequency of driving incidents and the severity, both actual and potential, of those incidents that did occur. It includes attention to BP employees and our contractors. In late 2001/early 2002, a driving incident reduction plan was put into place based on an analysis of previous year incident statistics. The incident reduction plan included a revised driver training program at the Schlumberger Driving School, implementing the Mission Possible at Work program (a partnership between the Alberta Motor Association and the RCMP) at all field and office locations, addressing driving issues with all major on-site contractors and finally developing and implementing leadership tools to promote better driving behavior. The result of the driving incident reduction plan implemented in 2002 were monitored at the end of each quarter and a revised plan put into place for 2003 based on data trends.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".