Investigating the effect of the deflection of the vertical on lidar observations
Bibliographic record
Abstract
Considerable materials have been generated that focus on the error analysis of light detection and ranging (lidar) derived coordinates through the direct georeferencing equation. One component of the equation, namely the deflection of the vertical (DOV), has been largely ignored within the literature. This rotational component serves to reconcile the ellipsoidal and local-level reference systems and is often considered to be insignificant. The sensitivity of lidar-derived coordinates to the deflection of the vertical was investigated through simulation. This is accomplished by deriving three-dimensional coordinates through the direct georeferencing equation and both ignoring and including the deflection of the vertical. Failure to consider this component was found to overcome commercially published horizontal accuracies at magnitudes of 34″, 35″, and 37″ for flying heights of 1000, 2000, and 3000 m, respectively, and vertical accuracies at 53″, 40″, and 35″ for flying heights of 1000, 2000, and 3000 m, respectively. Values of this magnitude are prevalent in mountainous environments and should not be ignored. Lastly, the unavoidable error existing in determinations of the deflection of the vertical was reported and was also determined to be significant with respect to the overall lidar error budget.
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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 | 0.001 | 0.004 |
| 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 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".