Resourcing the next industry defining spill
Bibliographic record
Abstract
Abstract The Deepwater Horizon (DWH) response was unprecedented in scale and complexity. In addition to testing the limits of Industry's technical knowledge, it required a sustained response of personnel effort over several years. At the peak of the response, some 47,000+ responders were deployed across five states. For any future incident of similar scale the challenges of resourcing must be considered now, to ensure a timely, efficient and effective response can be achieved. Whilst the contribution of every responder is important, it is clear that some command and field roles are more critical than others. For these key roles there are a limited number of individuals with the knowledge, experience, credibility and personality to successfully take them on. Furthermore, accessing these individuals - having up-to-date contact details, maintaining business continuity and assuring their competency - is a challenge. Another common preparedness gap is that most exercises do not test the process for mobilising people past the first few days (thereby not learning lessons about the time it takes) or consider the challenge of putting people in place with the right skill set during a prolonged response. DWH was resourced using the ‘little black book' of contacts from oil spill response organisations (OSROs), Oil and Gas operators, scientific experts and the local communities. Whilst successful, there were lessons to learn from the approach. In the last 10 years the expectations from regulators, public and other stakeholders on the speed, transparency and effectiveness of response have multiplied. To meet these growing expectations a more robust and efficient way of putting the right people, in the right place at the right time is required. This poster discusses the merits and suggests a potential mechanism for a globally aligned mutual response network. Oil spill response cooperatives are ideally positioned to identify key roles, the people who could fill them, assure their capability and readiness, and address the barriers which slow down mobilisation such as agreeing contracting terms and rates. This poster will lay out the challenges that both Industry and OSROs face in resourcing the next industry defining spill. It will set out how an oil spill mutual response network answers these questions. It will also reinforce why collaboration and cooperation, key principles of Tiered Preparedness and Response, will continue to be the most efficient and effective way of accessing capability and maximising Industry's preparedness to respond to the next big incident.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads agree on what is shown here.
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".