Estimating aerodynamic roughness (<i>z<sub>o</sub> </i>) in mixed grassland prairie with airborne LiDAR
Bibliographic record
Abstract
In this research note we show that airborne imaging light detection and ranging (LiDAR) is capable of estimating the aerodynamic roughness height (zo ) in a mixed grassland prairie. This is accomplished by establishing empirical relations between wind profile derived estimates of zo and vegetation height measurements from the airborne LiDAR data. We show that up to 65% of the variation of zo can be explained by LiDAR estimates of vegetation height and that up to 76% of the variation can be explained by the height variability of vegetation. We also show the effects of different filter sizes used to identify the ground surface and the top of the vegetation in the LiDAR point cloud data. Overall, results from this investigation are encouraging for future spaceborne LiDAR missions, especially in terms of the potential for providing new insight on spatial and temporal patterns of zo .
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".