Uncertainties in retrieved ice thickness from freeboard measurements due to surface melting
Bibliographic record
Abstract
Abstract Airborne and spaceborne remote sensing of ice freeboard offers a good method of retrieving ice thickness in the polar oceans. However, its accuracy is highly limited by the factors altering the hydrostatic equilibrium of ice floes, such as snow cover and melt ponds which change the surface loading on the ice volume. In contrast to the abundant studies on snow loads, little attention has been paid to the role of melt ponds, partly owing to the difficulties of freeboard measurements during the melt season. To help fill this gap and provide a basis for possible instruments and algorithms being able to access ice freeboard with melting surface in future, a theoretical model was developed to investigate the uncertainty in ice thickness retrieval due to surface melting. First, the ice thickness was related to the freeboard, snow depth, melt pond size and densities of snow, ice and water, and then a sensitivity analysis was carried out to study the influence of melt pond morphology. The results show that melting ice has a much lower mean thickness than ice without a melting surface, although with the same freeboard because of a loss of floe weight due to melting. During pond evolution, a floe gains weight when ponds deepen on the vertical scale, but loses weight when they widen on the horizontal scale, resulting in increasing mean ice thickness with decreasing pond depth and fraction. Freeboard is found to be the major source of uncertainty in the retrieved thickness of first-year ice (FYI), while it is ice density in the case of multi-year ice (MYI). The ratio of ice draft to freeboard ranges from 3.0 to 6.2 for FYI and 2.0 to 4.1 for MYI, agreeing with field observations during melting seasons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| 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".