Bibliographic record
Abstract
R&D on Campus University research and development is continuing at a steady rate, despite sustained low oil prices. For now, money allocated by the oil and gas industry in previous years is still available for many academic institutions. However, oil prices may eventually take a toll on funding if they remain low when it is time for companies to renew their existing commitments. “Next year is when we will look and see if we have our sponsors, or (determine if) some of them cannot continue,” said Manika Prasad, associate petroleum engineering professor at Colorado School of Mines. Other universities around the world are facing the same pressures and uncertainties. University of Aberdeen Located near the North Sea in one of the world’s major oil and gas capitals, the University of Aberdeen employs more than 150 professors working on energy-related research. “We are not just an oil and gas school at Aberdeen, we have a very broad range of sciences being taught and worked on. So, sometimes the interaction between them produces something unusual,” said John Scrimgeour, executive director of the university’s Aberdeen Institute of Energy. University of Alberta For decades, operators have attempted to use buried electromagnetic coils as an alternative to steamflooding in heavy oil reservoirs, but they consume a great deal of energy and take a long time to work. Tayfun Babadagli, who heads the University of Alberta’s Enhanced Oil and Gas Recovery and Reservoir Characterization group, is leading research to make electromagnetic heating a practical process with the use of nanotechnology. Colorado School of Mines As the industry’s ability to create numerical models of the subsurface continues to improve, one fact remains unchanged: Models are only as accurate as the data used to create them. Manika Prasad, associate professor of petroleum engineering at the Colorado School of Mines, started the Rock Physics and Petrophysics of Organics, Clay, Shale, and Sand initiative in 2011 to improve the accuracy of subsurface models by creating standards for rock physics and geomechanical data collection. Texas A&M University This year, Texas A&M University’s Crisman Institute for Petroleum Research has been shifting its research focus toward projects that will improve recovery in shale reservoirs. The institute was founded in 1982 with the broad directive of producing advances in upstream technology. However, the industry demand for goal-based research that offers a higher return on investment has led the faculty to narrow its mission.
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 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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.050 | 0.031 |
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".