Arctic Development Roadmap: Prioritization of R&D
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The development of oil and gas resources in harsh northern environments isdependent on the availability of the necessary knowledge and technology toovercome the challenges associated with operating in these regions. Tofacilitate this, research and development programs are needed in a number ofspecific areas. The new, industry-funded Centre for Arctic Resource Development(CARD) is focused on addressing medium and long-term R&D needs forpetroleum development in Arctic and sub-Arctic regions, and has sponsored abroad industry consultation program with subject matter experts from the Arcticoil and gas sector. This information has been distilled into an ArcticDevelopment Roadmap (ADR), in which R&D needs have been identified andprioritized to support effective planning. In this paper, the key findings ofthe Arctic Development Roadmap program are presented. Introduction The purpose of this project was to develop an Arctic Development Roadmap toidentify, organize and prioritize key R&D themes needed to fill gaps in theknowledge, technology, methodology and training associated with offshore Arcticoil and gas development. This work was funded through the C-CORE Centre forArctic Resource Development which is based out of St. John's, NL, Canada. CARDconducts medium to long-term R&D designed to improve the capacity andcapability for safe, responsible and cost-effective hydrocarbon development inArctic and sub-Arctic regions. With $16.5 million in combined funding from theHibernia and Terra Nova projects and the Research & Development Corporationof Newfoundland and Labrador (RDC), CARD will create more than 20 new full-timepositions for highly qualified individuals, from current world-class experts torising research stars. The centre's expertise is primarily engineering, thoughit will interface with experts in many fields, both in industry and academia. As indicated in Figure 1 below, the results of the ADR project are an importantinput into the five-year R&D plan for CARD, which has been vetted byindustry. This study will also serve to highlight research priority areas ofrelevance to the broader research community and various sectors of the oil andgas industry.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it