MABEL photon-counting laser altimetry data in Alaska for ICESat-2simulations and development
Bibliographic record
Abstract
Abstract. Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) is scheduled to launch in late 2017 and will carry the Advanced Topographic Laser Altimeter System (ATLAS), which is a photon-counting laser altimeter and represents a new approach to satellite determination of surface elevation. Given the new technology of ATLAS, an airborne instrument, the Multiple Altimeter Beam Experimental Lidar (MABEL), was developed to provide data needed for satellite-algorithm development and ICESat-2 error analysis. MABEL was deployed out of Fairbanks, Alaska, in July 2014 to provide a test dataset for algorithm development in summer conditions with water-saturated snow and ice surfaces. Here we compare MABEL lidar data to in situ observations in Southeast Alaska to assess instrument performance in summer conditions and in the presence of glacier surface melt ponds and a wet snowpack. Results indicate the following: (1) based on MABEL and in situ data comparisons, the ATLAS 90 m beam-spacing strategy will provide a valid assessment of across-track slope that is consistent with shallow slopes (< 1°) of an ice-sheet interior over 50 to 150 m length scales; (2) the dense along-track sampling strategy of photon counting systems can provide crevasse detail; and (3) MABEL 532 nm wavelength light may sample both the surface and subsurface of shallow (approximately 2 m deep) supraglacial melt ponds. The data associated with crevasses and melt ponds indicate the potential ICESat-2 will have for the study of mountain and other small glaciers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".