Slope Estimation from ICESat/GLAS
Bibliographic record
Abstract
We present a novel technique to infer ground slope angle from waveform LiDAR, known as the independent slope method (ISM). The technique is applied to large footprint waveforms (\(\sim\) mean diameter) from the Ice, Cloud and Land Elevation Satellite (ICESat) Geoscience Laser Altimeter System (GLAS) to produce a slope dataset of near-global coverage at \(0.5^{\circ} \times 0.5^{\circ}\) resolution. ISM slope estimates are compared against high resolution airborne LiDAR slope measurements for nine sites across three continents. ISM slope estimates compare better with the aircraft data (R\(^{2}=0.87\) and RMSE\(=5.16^{\circ}\)) than the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) inferred slopes (R\(^{2}=0.71\) and RMSE\(=8.69^{\circ}\)) ISM slope estimates are concurrent with GLAS waveforms and can be used to correct biophysical parameters, such as tree height and biomass. They can also be fused with other DEMs, such as SRTM, to improve slope estimates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".