Bibliographic record
Abstract
remediation. She represents ORNL on the Natural Gas and Oil Technology Partnership, working with the U.S. Department of Energy to foster collaborations between the national laboratories and industry. Oil and natural gas production is often accompanied by large amounts of wastewater [1]. The volumetric ratio of water-to-oil will increase over the lifetime of an operation and can eventually exceed 90%. Water is also associated with some, but not all, gas production. For instance, dry sources of gas are found in Alberta, Canada. Produced water is often reinjected into the well to increase oil recovery. However, in the western states, the injected water is supplemented by “clean ” groundwater—thus depleting a scarce resource. Water associated with fossil fuel production constitutes a high-volume waste stream, on the order of a trillion barrels of water a year [2]. Organic contamination from soluble and dispersed oil is monitored by the U.S. Environmental Protection Agency (EPA) for offshore production. Salinity, rather than organic contamination, is the primary concern for onshore discharge, although organics cause difficulties with some salt-removal methods, such as reverse osmosis. A thorough review of produced water issues and the research that has been undertaken to solve these problems was published in 2004 by Veil and coworkers [3]. Oak Ridge National Laboratory (ORNL) and other national laboratories, with the support of the U.S.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.456 | 0.250 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".