A comparison between coincident laser and Ku radar versus S- to C-band 'snow radar' data for airborne retrievals of snow depth on sea ice
Bibliographic record
Abstract
Snow depth estimates remain a large uncertainty for constraining the accuracy of sea ice thickness retrievals from polar altimetry. There have been several recent investigations into methods for estimating snow depth from airborne observations over sea ice; this poster outlines a comparison between two different methods applied to Operation IceBridge data from the Spring 2016 campaign. The first co-locates visible-band laser scanner data from the Airborne Topographic Mapper with Ku-band data from the CReSIS radar, using a fixed threshold first-maximum retracker algorithm for retracking radar waveforms and applying a calibration step to remove the vertical offset between sensors at leads. This method represents an airborne proxy for the newly-aligned ICESat-2 and CryoSat-2 orbits of the Cryo2Ice campaign. The second method uses the conventional CReSIS ultrawide-band frequency‐modulated continuous‐wave ‘snow radar’ system, that ranges between S- and C-band, applying the retracker algorithm described by Newman et al 2014. We evaluate properties of the estimated snow depth distribution, and alignment of air-snow and snow-ice interfaces, along the aircraft track and the scale of correlation between sensors.
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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 | 0.002 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".