Management of Implementation of Nanotechnology in Upstream Oil Industry: An Analytic Hierarchy Process Analysis
Bibliographic record
Abstract
Nanotechnology has had revolutionary effects in various fields of industry such as electronics, pharmaceuticals, and biomaterials. However, upstream oil industry has been noticeably slow in adopting the emerging technologies. This is mainly due to the exceptionally large investments needed to implement novel technologies in this industry. However, the projections for the increasing global energy demand require that oil and gas industry inevitably move toward adopting the emerging technologies. The high risk associated with enormous investments required for this aim necessitates measured and well-researched energy policies, with regard to the implementation of nanotechnology in the oil and gas industry. This paper presents a concise summary of the research reported in the literature on the potential benefits of nanotechnology in upstream oil industry. These applications were categorized into ten groups, and presented to a pool of experts, who judged on their relative importance with respect to various decision-making criteria. All this information was then compiled into a single matrix, which indicates the priority of each investment alternative with respect to every criterion in the form of a raw number. Finally, using a decision-making software package, a dynamic analytic hierarchical process (AHP) analysis was performed, providing a route to customized investment policies.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".