ICESat/GLAS Canopy Height Sensitivity Inferred from Airborne Lidar
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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 it