Multi-Year Ice Thickness: Knowns and Unknowns
Bibliographic record
Abstract
Nearly 5000 direct measurements of multi-year ice thickness, compiled from studies spanning a period of 51 years, are used to identify some of the “knowns and unknowns” surrounding multiyear ice. Many of these studies reside in the grey literature, which makes this paper one the few in the open literature to include data from these not-often-seen reports. Individual thickness measurements on multi-year ice suggest that floes from the Beaufort, Central Canadian Arctic, High Arctic and Sverdrup Basin are of comparable thickness. The thickest multi-year ice, 40.2 m, was measured on a pressure ridge in the Canadian Beaufort. On average, the mean thickness is 5.6 m (±2.2 m) for a relatively level multi-year floe and 9.9 m (±4.7 m) for a pressure ridge. Floes with a mean thicknesses of 11.3 m, and pressure ridges with a mean thickness of 24.7 m have been measured, however.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".