A Vehicle-Based Laser System for High-Resolution DEM Development – Performance Evaluation of System Components.
Bibliographic record
Abstract
Surface microtopography is quantified by the deviations in the direction of the normal vector of the surface. Soil microtopography is a major factor influencing soil erosion by water and wind. Surface microtopography can be accurately described using the Digital Elevation Model (DEM). In this study, a vehicle-based laser system was developed to generate high-resolution DEM data. The system consisted of five major components: a laser line scanner, a gyroscope sensor, a real-time kinematic GPS, a frame-rail mechanism, and a data acquisition and control unit. A series of experiments were conducted in the laboratory to evaluate the performance of the components. The result of distance measurements indicated that the system gave the most consistent distance measurement on a white paper. Static gyroscope sensor accuracy tests showed that angle measurement errors observed in combined pitch/roll rotations were larger than in single rotations. Within ±30º of single rotations, the measurement errors for pitch and roll angles were within 0.8º and 0.4º, respectively. The tests of the angular displacement on the linear rail showed that it slightly tilted towards the laser line scanner when it moved along the rail.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".