Bibliographic record
Abstract
Abstract GFREE stands for a multi-disciplinary target-oriented research program into unconventional gas technology that includes geoscience (G), formation evaluation (F), reservoir drilling, completion and stimulation (R), reservoir engineering (RE), and economics and externalities (EE). The program has concentrated on unconventional gas formations in Canada but the results are widely applicable to worldwide resources. The principles developed can be beneficially applied to other research programs related to the oil and gas industry. The multidisciplinary approach has meant that graduate students are not supervised, in general, by only one professor. Rather, the students have been mostly co-supervised by 2 professors with different backgrounds and/or by professionals working in industry. The program has been supported by industry, government and the university and cooperation amongst GFREE and these organizations has proven to be of utmost importance. A decision made at the beginning, which has proved valuable for this program, was that the team would concentrate on issues with potential to generate positive cash flows. GFREE students generally share in the same study areas promoting discussion and collaboration. Bi-weekly meetings of the team members allow for exchange of ideas and dry runs that help students prepare for their thesis defenses and/or presentation of papers in local and international conferences. Undergraduate and graduate courses that concentrate on unconventional gas exploitation and naturally fractured reservoirs have proven valuable to train students that join this program and to disseminate results of the research. Flexibility has proven also to be of importance. As GFREE was developed as a 5-year program it was impossible to anticipate all the hurdles and bridges that would have to be crossed. For example, the anticipation of core availability for the study areas did not always materialize. Consequently it was necessary to switch to the study of drill cuttings for qualitative and quantitative evaluation with limited support of core data. The multi-disciplinary nature of the GFREE program has led to valuable insights. It is concluded that the integration of many disciplines and the involvement of government, industry and academia has provided significant positive results that could be extended to other disciplines.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.353 | 0.207 |
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".