Delineating Hummocky Terrain: A Field-Based Approach
Bibliographic record
Abstract
A geomorphological study was conducted to determine the most accurate and efficient number of primary data points necessary for modelling hummocky terrain with subtle elevation changes. Primary elevation data were collected on hummocky terrain using an S320 GNSS survey receiver, interpolated using ordinary kriging in ArcGIS, and analyzed using a Monte Carlo simulation in MATLAB. This analysis was done to suggest an ideal number of sample points required to produce a highly accurate digital elevation model. From research conducted in Georgetown, ON, it was determined that 20,000 points should be sampled per square kilometer, but the findings can be altered slightly to best suit future geomorphological and hydrological studies. The findings will increase the understanding of the subtle relationship between topography and interpolation error and will guide future data capture and modelling of terrain with subtle elevation variation of less than 10 m. Results can influence the scope of point collection and the clustering of points or boreholes across terrain with variable elevation changes. There is also a potential for application in regions where placing boreholes would be difficult or costly when trying to understand subsurface geometries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".