Analysis of LiDAR data for fluvial geomorphic change detection at a small Maryland stream
Bibliographic record
Abstract
Numerous detailed topographic measurements, which must be periodically repeated, are required to characterize stream bank and channel geometry. Light Detection and Ranging (LiDAR) is becoming more widely used, but its accuracy for change detection in and around small streams is not well quantified. Two LiDAR and one ground-surveyed elevation data sets are compared for a thickly vegetated riparian area in the Maryland Piedmont. Interpolated surfaces (prediction maps) and estimates of their uncertainty (standard error maps) are created from the point data using kriging. The LiDAR 2006 elevations are compared to ground-survey to evaluate accuracy. LiDAR 2002 and 2006 elevations are compared to evaluate the potential for change detection. When the estimated LiDAR system error is included in hypothesis testing, no statistically significant elevation differences are found between 2002 and 2006. Conclusions about geomorphic change based on LiDAR scenes should account for error and uncertainty in the data collection and processing.
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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 | 0.001 |
| 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".