Fiery Ice from the Seas - The First Workshop of the International Committee on Methane Hydrates
Bibliographic record
Abstract
This document reviews an expert workshop on methane hydrates, held in Honolulu, Hawaii, at the International Imin Conference Center of the East West Center on March 7-9, 2001. The workshop was organized by the Hawaii Natural Energy Institute of the University of Hawaii and the Environmental Quality Sciences Section of U.S. Naval Research Laboratory, in cooperation with the Hokkaido National Industrial Research Institute of Japan's Agency of Industrial Science and Technology, and Inha University of Korea. Fifty-eight participants from the United States, Japan, Korea, Canada, Norway, Russia, and the United Kingdom attended the workshop. Participation was by invitation only. Attendees included some the world's leading researchers in methane hydrates, and representatives from government agencies and the private sector. The principal workshop objectives were: (1) review past, ongoing, and planned methane hydrates R&D projects and programs; (2) share information on budgets and research resources and priorities in different countries; and (3) establish linkages for domestic and international partnering. The program of the 2-1/2 day workshop included plenary lectures, panel discussions, small group breakout meetings, and a poster session. It was conducted as a working event where all participants conferred to develop a roadmap for future collaborative studies of methane hydrates.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.005 |
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".