On the uncertainty of sea-ice isostasy
Bibliographic record
Abstract
Abstract During late winter 2007, coincident measurements of sea ice were collected using various sensors at an ice camp in the Beaufort Sea, Canadian Arctic. Analysis of the archived data provides new insight into sea-ice isostasy and its related R-factor through case studies at three scales using different combinations of snow and ice thickness components. At the smallest scale (<1 m; point scale), isostasy is not expected, so we calculate a residual and define this as Ж (‘zjey’) to describe vertical displacement due to deformation. From 1 to 10 m length scales, we explore traditional isostasy and identify a specific sequence of thickness calculations which minimize freeboard and elevation uncertainty. An effective solution exists when the R-factor is allowed to vary: ranging from 2 to 12, with mean of 5.17, mode of 5.88 and skewed distribution. At regional scales, underwater, airborne and spaceborne platforms are always missing thickness variables from either above or below sea level. For such situations, realistic agreement is found by applying small-scale skewed ranges for the R-factor. These findings encourage a broader isostasy solution as a function of potential energy and length scale. Overall, results add insight to data collection strategies and metadata characteristics of different thickness products.
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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 | 0.011 | 0.033 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".