Digital elevation modelling of soil type and drainage within small forested catchments
Bibliographic record
Abstract
This paper deals with predicting flow accumulation, drainage class, and soil and vegetation type within small headwater catchments (<20 ha) of two contrasting forested areas in northern New Brunswick, Canada, based on digital elevation modelling. A digital elevation model (DEM), with a point sampling resolution of about 75 m, was used to derive local flow accumulation and slope gradients. These calculations were then compared with direct field mapping of flow accumulation and slope gradients, involving on-site tracking of watershed boundaries, ridges, hummocks, depressions, gullies, and stream channels. In general, the resulting DEM-derived and field-assessed flow accumulation values did not differ statistically from each other. Subsequently, these values were analyzed as potential predictors for soil wetness, drainage, and soil and vegetation type as determined for 77 small forest plots, all scattered throughout the catchments at locations of increasing flow accumulation. In this analysis, the field-assessed flow accumulation values were found to be better predictors than the DEM-derived values . It is suggested that the DEM-derived predictions would be much enhanced by an increased DEM resolution, and that this increase would lead to particularly reliable estimates for local soil wetness, drainage, and soil and vegetation type in catchments with low substrate permeability. Key words: Flow accumulation, soil wetness index, soil taxonomic units, soil drainage, soil permeability, digital elevation model
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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".