An Improved Method of Extremal Value Analysis of Arctic Sea Ice Thickness Derived From Upward Looking Sonar Ice Data
Bibliographic record
Abstract
Abstract In the Beaufort Sea, observations of extreme draft sea ice features havebeen identified from upward looking sonar (ULS) datasets spanning severalyears. Using analysis methods from extreme value theory, the estimated 100-yearreturn values of the maximum ice draft have been derived. In addition, theapplicability of these statistical techniques to the Northeast Greenland iceregime is examined using one year of ULS data at two locations from 2008 to2009. The methods have been developed for the Beaufort Sea region andsubsequently, further refined for use in estimating extreme ice hazards offNortheast Greenland. These estimates provide inputs to the design of offshoreplatforms and ships in support of oil and gas activities in these ice-infestedwaters. Previous studies in the Canadian Beaufort Sea derived an empirical upperlimit on the maximum sea ice thickness resulting from deformation processesbased on the relationship of maximum ice thickness as a function ofsimultaneous values of undeformed ice thickness. Using the more extensive ULSice keel data sets now available, these methods were re-evaluated and updated. Similar analyses were carried out on ice thickness measurements obtained offNortheast Greenland which reveal distinct differences in the ice regime ofthese two geographical areas. Improvements to extremal value statistical analysis methods for longrecurrence intervals of 100 years for ice draft (D100) are based on the threeparameter Weibull distribution which has been optimized for application to verylarge sea ice keels using a peak over threshold selection approach. Theseresults were compared to the maximum draft limit and undeformed ice thicknessrelationship. We developed techniques to refine at a high resolution the lowerthreshold on maximum draft and examine the implications of this filtering onD100. This is an important consideration as selecting the lower maximum draftthreshold is a balance between retaining enough observations to ensurestatistical robustness and sampling only the extreme tail of the maximum draftdistribution. Methods for performing these statistical analyses arepresented.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".