Bibliographic record
Abstract
Guest editorial It is widely accepted today that for the remainder of Hydrocarbon Era, global energy stability will rely increasingly upon crude oil and natural gas recovered from giant offshore fields, many of which will be located far from major markets, in hostile environments such as ultradeepwater and the Arctic. Less understood is the fact that large-scale, multibillion-dollar developments are precisely where the principal technological and economic drivers to push beyond technological boundaries are the strongest and where stakeholder participation and partnering is most complex. The different agendas of the various partners and stakeholders in such developments often compound the challenges of technological complexity. For example, a national oil company typically is seeking to ensure long-term domestic political stability and to invigorate the national economy with development of natural resources. A top-tier international oil company is generally aiming to maintain global production, improve unit production costs, and maintain reserves, while, for most smaller operators, the drivers tend to be rapid and reliable realization of assets that feed dynamic balance sheets. In the case of technology providers—namely vendors, constructors, and fabricators—the drivers tend to be stretching applications of established technology, early capitalization of investment in new equipment/assets, maintaining competitiveness, and vying for market share. Against a backdrop of daunting technological challenges, operators, equity owners, and other stakeholders in demanding frontier developments frequently differ on such fundamental issues as contracting strategies; the timing of project milestones; or the program for technology selection, maturation, and implementation. Opinions are often divided, also, over how to balance the costs and risks of technology selection, or the extent of a given technological stepout. Such disagreements are compounded by the very different business drivers and risk appetites of project partners, stakeholders, and technology providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".