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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".