Workforce Training on Marine Mammals and Sound: Progress and a Way Forward for an Advanced Joint Industry Course
Bibliographic record
Abstract
Abstract In 2008, during the SPE International Conference on Health, Safety and the Environment in Nice, France, BP p.l.c. (BP) presented "Managing Marine Mammal Issues: Corporate Policy, Stakeholder Engagement, Applied Research and Training." That paper recognized the need for oil and gas industry staff to develop a basic understanding of the potential effects of underwater sound on marine mammals and offered a short description of one of BP's approaches to familiarization training. Since 2008, stakeholder concerns and regulatory requirements have grown. Today, the oil and gas industry needs employees familiar with the issue as well as a smaller cadre of employees with substantial expertise. This is especially true in areas of relatively new exploration and development activity, such as southern Australia and northern Canada, and in mature areas faced with rapidly-changing regulatory requirements, such as the Gulf of Mexico. Recognizing the importance of this issue, BP has worked towards improving workforce competency through a variety of methods, including an intranet-based familiarization course, third-party courses, internal knowledge-sharing events, and mentoring. While each of these approaches plays an important role in workforce development, the need for an advanced training course focused specifically on industry needs and drawing from industry examples remains. This paper describes an example of an existing BP training program and advocates joint industry development of an advanced training course.
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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 | 0.015 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.031 | 0.007 |
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".