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
E&P Notes Nanotechnology Could See Big Future in Water Cleanup Joel Parshall, JPT Features Editor Nanotechnology could have a big future as a tool for upstream oil and gas and other industries to use to clean up contaminated water, Professor Michael S. Wong of Rice University, Houston, told the SPE Gulf Coast Section’s R&D Study Group recently. Wong, chair of the university’s chemical and biomolecular engineering department, said that the multidisciplinary nanotechnology field has sufficiently matured to enable researchers and practitioners to envision real prospective solutions to water contamination problems. Water is by far the largest byproduct of the fossil fuel industry. Wong’s presentation noted that in the US, oil industry well operations produce in aggregate approximately 10 times as much water as they do oil, and in Canada the water/oil ratio is 14 to 1. Workforce Education Key To Understanding Drilling Data Stephen Whitfield, Senior Staff Writer In trying to remove risk and uncertainty from drilling and improve their overall drilling efficiency, operators are developing more reliable analytic capabilities and adopting novel sensor and data-streaming technologies to help them process the massive amounts of data coming from their wells. An industry expert said that workforce education will be critical to helping companies adapt to a changing data landscape and optimize their operations. At a presentation held by the SPE Drilling and Uncertainty Technical Section, Eric van Oort discussed the issues involved in analyzing data for drilling optimization, and the work being done at the University of Texas at Austin to help ease the process. Van Oort is a professor of petroleum engineering at the UT-Austin and a former onshore gas technology manager at Shell. Digital Image Correlation: A New Way To Look at Hydraulic Fracturing Trent Jacobs, JPT Digital Editor Digital image correlation (DIC) is routinely used in modern mechanical engineering to analyze the strength of building materials. Geologists have used the technology for the same reason in the study of mines. Now, researchers from the University of Louisiana at Lafayette are making the case that DIC can also help petroleum engineers—specifically those in the business of hydraulic fracturing. The ultimate aim: an index of unconventional rock types based on a quantification of their ability to be stimulated, or what oil and gas producers simply call “fracability.” DIC technology has a few variations, but this application involved the coupling of a high-speed camera with commercial change-tracking software. This simple approach allowed researchers to see frame-by-frame how lines of strain building up inside compressed rock samples directly correlated to where fractures would form a few seconds later.
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 | 0.001 | 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