NE Greenland Ice Study Group and Recent Project Work
Bibliographic record
Abstract
Abstract The region that lies off the northeast coastline of Greenland is of high interest to the oil industry. This is a very remote area, well known for the severity of its physical environment. Factors of note include a limited open water season, heavy pack ice conditions, icebergs, low temperatures and poor visibility. The constraints that are imposed by this environment will present significant challenges for various oil industry activities such as seismic data acquisition, exploratory drilling operations, and potential development and export systems. However, good data on the physical environmental conditions in this region are sparce. In order to improve the current state of knowledge, a NE Greenland Ice Study Group was formed to conduct "directed ice R&D work" of relevance to oil industry needs. The concept for this group was based on a collaborative approach successfully used in Canada during the 1970's and 1980's. The NE Greenland group includes 12 oil companies that jointly discuss and identify key ice information requirements, together with the Bureau of Minerals and Petroleum (BMP), and support ice related R&D on a proportionate basis. The approach has proven to be a sensible and cost effective way to conduct work and make technical progress in a new region like the NE Greenland offshore, and has also been used by similar industry groups in the Disko West and Baffin Bay regions of Greenland. This paper highlights the NE Greenland study group approach that has been developed, which is a good model to consider for other Arctic offshore areas, along with the NE Greenland ice studies conducted by our group to date. It also highlights some of the results of the NE Greenland ice study group projects carried out so far.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".