Rural Alaska Coal Bed Methane: Application of New Technologies to Explore and Produce Energy
Bibliographic record
Abstract
The Petroleum Development Laboratory, University of Alaska Fairbanks prepared this report. The US Department of Energy NETL sponsored this project through the Arctic Energy Technology Development Laboratory (AETDL) of the University of Alaska Fairbanks. The financial support of the AETDL is gratefully acknowledged. We also acknowledge the co-operation from the other investigators, including James G. Clough of the State of Alaska Department of Natural Resources, Division of Geological and Geophysical Surveys; Art Clark, Charles Barker and Ed Weeks of the USGS; Beth Mclean and Robert Fisk of the Bureau of Land Management. James Ferguson and David Ogbe carried out the pre-drilling economic analysis, and Doug Reynolds conducted post drilling economic analysis. We also acknowledge the support received from Eric Opstad of Elko International, LLC; Anchorage, Alaska who provided a comprehensive AFE (Authorization for Expenditure) for pilot well drilling and completion at Fort Yukon. This report was prepared by David Ogbe, Shirish Patil, Doug Reynolds, and Santanu Khataniar of the University of Alaska Fairbanks, and James Clough of the Alaska Division of Geological and Geophysical Survey. The following research assistants, Kanhaiyalal Patel, Amy Rodman, and Michael Olaniran worked on this project.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".