E&P Industry's Challenges with Managing Mitigation Guidelines for the Protection of Marine Life During Marine Seismic Operations
Bibliographic record
Abstract
Abstract Over the past decade, potential impacts from anthropogenic sound in the marine environment have received increased attention. The oceans are home to a diverse ecosystem and serve competing uses such as marine habitat, mineral resource operations, shipping, fishing, tourism and recreation. All human activities conducted in the oceans should be carried out in an environmentally responsible manner to ensure sustainability of the oceans. The E&P Industry operates globally in the offshore sector. Seismic survey operations are one source of anthropogenic sound in the marine environment and have received increased scrutiny over the years from regulators, environmental non-governmental organizations and the public. Though three decades of world-wide seismic surveying and various research projects provide no evidence to suggest direct physical injury to a marine mammal species, mitigation measures are commonly implemented to further reduce the level of potential risk of harm to marine mammals. The E&P Industry is committed to conducting offshore activities in an environmentally responsible manner, including complying with mitigation and monitoring regulations and guidelines. However, Industry believes that mitigation measures should be based on risk assessment and the best available science instead of the commonly applied precautionary principle. To date, several countries including Australia, Brazil, Canada, Ireland, New Zealand, the United Kingdom, and the United States have implemented regulations or guidelines which specify mitigation measures for the protection of marine life when conducting seismic operations in their territorial waters. These mitigation measures vary from country to country while in many geographic regions, there are no guidelines in place. Operating globally, the Industry faces many challenges as it seeks to understand and implement these mitigation measures. This paper seeks to present a brief summary of current guidelines by region and discuss a recommended approach to mitigation measures.
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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 | 0.059 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.013 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".