Estimating the ice thickness of the Müller Ice Cap using an inversion of the shallow ice approximation
Bibliographic record
Abstract
The ice thickness of the Müller Ice Cap, Arctic Canada, is estimated using regression parameters obtained from an inversion of the shallow ice approximation by the use of a single Operation IceBridge flight line in combination with the glacier outline, surface slope, and elevation. The model is compared with an iterative inverse method of estimating the bedrock topography using PISM as a forward model. In both models the surface elevation is given by the Arctic Digital Elevation Model. The root mean squared errors of the ice thickness on the ice cap is 131 m and 139 m for the shallow ice inversion and the PISM model, respectively. Including the outlet glaciers increases the root mean squared errors to 136 m and 396 m, respectively. The simplicity of the shallow ice inversion model, combined with the good results and the fact that only remote sensing data is needed, means that there is a possibility of applying this model in a global glacier thickness estimate by using the Randolph Glacier Inventory. Most global glacier estimates only provide the volume and not the ice thickness of the glaciers. Hence, global ice thickness models is of great importance in quantifying the potential contribution of sea level rise from the glaciers and ice caps around the globe.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".