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 slow pace of technology acceptance is a concern for many in the oil and gas industry. A group of over 90 executives and leaders of the industry gathered in mid-March to discuss and analyze the causes and recommend steps to accelerate technology acceptance. Six issues were identified as determining factors for rate of technology acceptance in this industry. Each was discussed in depth during half-day-long breakout sessions and results are presented in companion papers by other authors1,2,3,4,5,6. The group also had a number of summary recommendations for accelerating technology acceptance. These were: Encourage active participation of company leadership Create technology-receptive company cultures Focus on value proposition Create incentives and rewards for successful use of technology Introduce mechanisms to reduction risk for early adopters Align the goals of operators and service companies Increase funding and involvement of venture capital for technology Encourage oil industry personnel to be more receptive to technology Communication of success stories more effectively This paper provides the historical background of the topic and presents a list of general items recommended by the group. Discussion of each specific topic of the breakout sessions along with results and recommendations are presented in companion papers.
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.
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