International arctic research programs : presented at seventh International Conference and Exhibition on Offshore Mechanics and Arctic Engineering, Houston, Texas, March 1988, Session on arctic research programs
Bibliographic record
Abstract
This special publication documents presentations at a special session on national arc tic research programs.This special session was organized by the Polar Technology Work ing Group, an informal body of individuals from the United States, Canada, Finland, Denmark, Norway and Sweden.The authors of the presentations informally represented their government-related activities.Government programs on polar technology (of the Arctic and Antarctic) appear to be scattered throughout many agencies in many coun tries.Interactions at an international level among the governments on the technology is sues are slowly evolving.This session brought major parties together in exchange of gov ernment (and some industry) research activities, policies and expenditure allocations.It is hoped that this first session will open a door for international consultation and cooper ation in effective technology transfer in the future, as well as informing the general public and industry on arctic and antarctic research activities.There are many opportunities for coordinated research among these nations to significantly extend the return on invest ment received for the resources applied in this area.It is hoped that such presentations will continue in the future.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.123 | 0.031 |
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".