Bibliographic record
Abstract
Reducing Vehicular Incidents with a Road Hazard Analysis N. Campbell; N. Campbell Schlumberger Search for other works by this author on: This Site Google Scholar D. Tate D. Tate Schlumberger Search for other works by this author on: This Site Google Scholar Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. Paper Number: SPE-86830-MS https://doi.org/10.2118/86830-MS Published: March 29 2004 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Campbell, N., and D. Tate. "Reducing Vehicular Incidents with a Road Hazard Analysis." Paper presented at the SPE International Conference on Health, Safety, and Environment in Oil and Gas Exploration and Production, Calgary, Alberta, Canada, March 2004. doi: https://doi.org/10.2118/86830-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE International Conference and Exhibition on Health, Safety, Environment, and Sustainability Search Advanced Search Abstract Vehicular related fatalities and injury rates are steadily increasing in our society. Despite all efforts, driving continues to rank as one of the highest risk activities in most oil and gas production or service companies.The question is: "What can we do about it?" Modern safety management routinely touts hazard awareness as one of the best accident prevention solutions in the traditional workplace. This same solution could pay big dividends if correctly applied to driving; hence the concept of road hazard analysis (RHA).This paper discusses an RHA program that was implemented in one segment and location of a major oilfield service company, resulting in decreased recordable auto accident frequency from 11.2 incidents/million kilometers in Q1 2002, to 0 in Q1 2003. Keywords: upstream oil & gas, incident, signal strength, journey, hazard register, pretrip planning, application, hazard, vehicle, rha Subjects: Safety, Risk Management and Decision-Making, Operational safety, Risk, uncertainty, and risk assessment Copyright 2004, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".