Toward the modeling of enhanced basal melting in ridge keels
Bibliographic record
Abstract
Observations from sonar data have suggested enhanced melting of thick, ridged ice relative to level ice. There are several mechanisms that may account for this intensified melting. In this paper, we examine the effects of two‐dimensional (2‐D) heat conduction and enlarged basal surface area due to the sloping sides of the keel on heat conduction and melt rates. The cross section of the 2‐D ridge is taken to be an isosceles triangle with a rounded crest. This is roughly the shape observed and allows a convenient numerical representation in polar coordinates. For comparison, ridges of similar shape are also represented as a collection of 1‐D columns of varying thickness, similar to what is implicit in typical ice thickness distribution models. The results show that 2‐D ridges inhibit the heat conduction compared to 1‐D ridges owing to the dominating effect of weaker temperature gradients. The slope of the keel is the dominant factor in determining the temperature gradient. A size distribution of 2‐D ridges reduces heat transfer to the atmosphere by 3 W m−2 compared to a similar distribution of 1‐D ridges. Over an annual cycle, basal ablation along the keel is insignificant for 2‐D ridges with small slopes, whereas ridges with large slopes show ablation rates determined by the ice‐ocean heat flux. These melt rates imply a transition from a triangular to a more rounded shape. The 1‐D ridge geometry is not adequate to simulate the net melting at the keel base over an annual cycle. Melt rates are calculated along the ridge keel and for level ice over a 40 day period for comparison with observations. For 1‐D ridges, all ice thicker than 5 m melts more slowly than the corresponding level ice. The inclusion of 2‐D heat conduction increases the amount of ablation in the thicker ice relative to the 5 m ice, especially for ridges with larger slopes. However, this increase explains only a small fraction of the enhanced basal melting seen in the observations. These results suggest that other mechanisms are important in determining the mass loss from thicker ice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".