Evaluation of Floating Wind Technology to Reduce Emissions in Newfoundland and Labrador's Offshore Hydrocarbon Industry
Bibliographic record
Abstract
Abstract The move to reduce greenhouse gas emissions in the offshore hydrocarbons production industry has resulted in a growing interest in the possibility of using offshore wind to reduce on-platform power generation. While some offshore areas are progressing towards, or planning for, the use of offshore wind to electrify hydrocarbon producing platforms, they do not have some of the challenges associated with Newfoundland & Labrador's offshore environment. The authors are undertaking a study to investigate the feasibility of, and the benefits associated with the use of offshore floating wind to displace power generation for offshore hydrocarbon production platforms, thus reducing GHG emissions. The work is focusing on the applicability of potential concepts, services, supply chain, fabrication, facilities, and operations, and how these tie into various floating wind concepts and technologies that might be fabricated and assembled locally, and operated offshore Newfoundland & Labrador (NL). Electrification of offshore oil and gas production facilities through offshore wind could reduce the requirement for local power generation via turbine generators under normal operation. This paper examines the suitability of potential offshore floating wind concepts in the NL offshore, using wind energy to supply power to offshore facilities, reducing the need for fuel powered turbine generators, and thereby decreasing GHG emissions from power generation. The study looks at the full-field approach, from suitability of design to construction to operations and maintenance of offshore wind technology.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".