Accelerating Technology Acceptance: Prioritization and Assessment of Technology
Bibliographic record
Abstract
Abstract This paper represents a summary of the discussion and findings from a breakout session held during the two-day SPE Applied Technology Workshop on Accelerating Technology Acceptance in the industry. Besides understanding current practices, the purpose of the session was to prioritize the steps that should be taken to achieve this goal, and to outline how best to assess the new technology needs of the industry. In addition to outlining the most salient points from the discussion on existing practices among oil companies and technology providers, also addressed are a series of recommendations for accelerating the acceptance of technology. The analysis includes a prioritization of the various suggestions given during this session, with a comparison made of the importance placed on each recommendation by operators versus technology providers. Some differences in opinion did exist. Based upon all information collected, it is clear that the road to accelerated technology acceptance involves commitment from leadership in both oil and technology provider companies, clear identification of the value propositions existing for new technology, and better communication between users and technology developers to insure that the appropriate R&D efforts are made to address the increasingly stringent exploration and production applications around the world. How SPE can assist in this important effort is also addressed.
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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 | 0.078 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".