Bibliographic record
Abstract
Introduction Ice fails under compressive loads in a variety of situations. During the exploration and extraction of oil and gas from ice-infested waters, for instance, risers must be protected against impact, necessitating a defense system sufficiently strong to resist both local and global failure. The forces can be large. The 110-meter wide Molikpaq exploration platform, deployed in the Canadian Arctic, experienced on 12 April 1986 interactions with a multi-year hummock 8–12 meters in thickness that led to global loads that were estimated to have reached 420 MN (Frederking and Sudom, 2006) or about 68% of the design load (Klohn-Crippen, 1998). Similarly, the 100-meter diameter Hibernia platform, now in production in the North Atlantic Ocean approximately 315 kilometers southeast of St. John's, Newfoundland, was designed and built to withstand global ice loads of about 1300 MN that could arise through interactions with icebergs (Hoff et al ., 1994). In these instances and others like them the limiting load was/is set by brittle compressive failure of the ice (Chapter 14). Compressive loading threatens not only the integrity of off-shore structures, but also the arctic sea ice cover itself. The cover extends over an area of about 12 million km 2 and plays a significant role in both local and global climate (e.g., Zhang and Walsh, 2006, and references therein). It is loaded under compression by wind and ocean currents (Thorndike and Colony, 1982). When strong enough, this forcing induces stresses that break the ice, through processes we discuss in Chapter 15.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".