Management: Progress and Challenges for Health, Safety, and the Environment in Exploration and Production (February 2006)
Bibliographic record
Abstract
Introduction A computer-based survey was taken of the program committee members of the first Asia Pacific Conference on Health, Safety, and Environment (HSE) in Oil and Gas Exploration and Production, which was held in Kuala Lumpur during 19–21 September 2005. The committee members, experts in their fields, were asked to express their opinions on the progress of the industry on HSE issues over the past 5 years and the effort that the industry should make over the next 5 years. This was the second time this survey has been conducted. The first time was at the SPE Seventh International Conference on HSE held in Calgary during 29–31 March 2004, and the results were reported in the July 2004 issue of JPT.1 The Kuala Lumpur results and the Calgary results are presented in the tables below. It should be noted that informal surveys were taken of the program committee members of the previous six SPE international conferences on HSE starting in 1991, but they were not in the same format as the two conferences mentioned above, so a quantitative comparison is not possible. Methodology The committee members were asked to complete a web-based questionnaire with the following questions:How would you rate the progress made by the industry in the following subjects over the past 5 years (excellent, good, moderate, poor, very poor)?How would you rate the effort that the industry should make in the next 5 years (much more effort, more effort, same effort, less effort, much less effort)?How would you rate the effort regulators should make in the next 5 years?How would you rate the effort that nongovernmental organizations (NGOs) should make in the next 5 years? The method of scoring and ranking is summarized in the appendix. A summary of the status of the industry programs is shown in Table 1, with the full results in Tables 2–4.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".