ICESat/GLAS Canopy Height Sensitivity Inferred from Airborne Lidar
Bibliographic record
Abstract
Variations in laser properties and data acquisition times introduced inconsistencies in Geoscience Laser Altimeter System (GLAS) data. The effect of data inconsistencies, on two GLAS height retrieval methods, from three study sites, are investigated and validated against airborne laser scanning (ALS) percentile heights, from three data sources: all/first return point clouds, and raster canopy height models. GLAS/ALS controls were established as a basis against which the influence of laser number, transmission energy, and seasonality were assessed through comparison statistics. The favored GLAS height method best compared with ALS 95th percentile heights from an all return point cloud. Optimal GLAS data (R2 = 0.69, RMSE = 8.10 m) were noted when GLAS acquired data during summertime from high energy, laser three transmissions. As GLAS data can be used in global biomass assessments, there is a need to understand and quantify the influence of these data inconsistencies on canopy height 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 | 0.002 | 0.005 |
| 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 | 0.000 | 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".