Deepwater Newfoundland and Labrador: Technology Application and Opportunities for Exploration and Production
Bibliographic record
Abstract
Abstract There has been considerable interest in deepwater opportunities offshore Newfoundland and Labrador (NL). The significant discovery of Mizzen, Harpoon and Bay du Nord fields by Statoil and partner Husky Energy, as well as the regional seismic data and metocean characterization projects by Nalcor, has generated industry interest in the area resulting in successful calls for bids in the deepwater region by the Canada Newfoundland Offshore Petroleum Board (CNLOPB) in 2014 and 2015. With the recent exploration license activity and significant deepwater discovery, the potential for further exploration and near-term development is rapidly approaching. There has been extensive operations experience and research and development within the shallow waters of continental shelf Jeanne d'Arc Basin over the past 40 years. With the progression of opportunities for exploration and development in deep water, the operations experience and applied research proven in shallow water can be utilized and adopted to deep water. The technical aspects of Remote Sensing, Ice Engineering, Ice Management and Geotechnical Engineering will be reviewed and discussed in the context of deepwater developments and opportunities for enhancement of technology will be presented.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".