Bibliographic record
Abstract
The Beaufort Sea is located north of Northwest Territories, the Yukon and Alaska, and west of Banks Island.The centre and northern part of this sea is frozen solid all year round, and the sea is exposed to considerable ice scour.Petroleum exploration in the ice packed Beaufort Sea in the 1960's led companies to develop offshore structures that would withstand extreme ice loading.In 1984 Gulf Canada Resources Ltd. deployed a mobile arctic caisson into the Beaufort Sea called the Molikpaq.The Molikpaq was installed on an artificially constructed berm, where it completed exploratory drilling during four subsequent winters.At the time that the Molikpaq was designed, ice loading on structures was a little known matter.To alleviate this knowledge gap, hundreds of sensors were installed on the Molikpaq to observe and attempt to predict behaviour due to loading.In 1986 the Molikpaq platform was brought to within minutes of failure due to cyclic loading.In this particular event the ice loading occurred at such a frequency that vibration occurred, causing liquefaction of the inner sand core.The sensor readings and detailed observations of this event and the various other loading encounters on the Molikpaq have provided a considerable amount of information for future projects.This paper compiles information on the design and installation of the Molikpaq in the Beaufort Sea.This drilling structure had various successes in regards to resisting ice loading, but eventually came near failure due to the unfamiliar characteristics of cyclic loading.This event will be explored, noting the lessons learned, and how this has affected and will continue to affect other offshore structures deployed in ice-filled waters.1
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".