Digital terrain analysis and landform segmentation for spatial variability of forest soil and litter properties in a deciduous forest stand in southern Ontario
Bibliographic record
Abstract
Intensive forest management requires spatial information of land properties at scales finer than those depicted in most conventional surveys, maps, and databases. Landform segmentation, a branch of Digital Terrain Analysis that groups similar topographic attributes into larger spatial units called landform element complexes (LECs), may provide an efficient, quantitative approach for modeling spatial variability at the scales relevant to land planners and managers. Landform segmentation was used in a deciduous forest stand on the Oak Ridges Moraine in southern Ontario, Canada in order to examine effects of topography on soil and stand properties used in indices of soil quality and stand productivity. Significant differences were recorded between the LEC spatial units in what is conventionally considered to be a homogenous forest stand on one soil unit. Fine-scale spatial maps of the results were constructed to demonstrate improvements over conventional sources of soil and land information such as coarse-resolution 2-D maps.
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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 | 0.001 | 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".