Bibliographic record
Abstract
Surface roughness is frequently measured using DEMs to characterize the ruggedness and topographic complexity of landscapes. Roughness maps have been applied in geological mapping, ecological modeling, and other environmental applications. These maps are typically derived using a roving-window approach, where kernel size dictates the scale at which roughness is assessed. The pattern of roughness is strongly scale dependent and this roughness-scaling relation can reveal useful information about the geomorphologic character of landscapes. This study applied hyper-scale analysis of a normal-vector based roughness metric for a LiDAR DEM of Rondeau Bay, Canada. The use of integral images, a data structure for computationally efficient filtering operations, allowed for the fine scale resolution of the analysis. The unique roughness scale signature of each grid cell in the DEM was derived for all spatial scales ranging from 3 to 5000 cells (7.5 m to 12,502.5 m). Maps of maximum roughness and the scale of maximum roughness were created for the study site. This cell-specific scaling approach to the characterization of surface roughness is in contrast to the use of single, often arbitrarily selected, kernel sizes to map topographic attributes. The additional information provided by the scale map was found to provide valuable ancillary data for landscape interpretation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".