Spatial organization of process domains in headwater drainage basins of a glaciated foothills region with complex longitudinal profiles
Bibliographic record
Abstract
Lithologic transitions and glaciations create complex longitudinal profiles that control contemporary erosion and deposition processes. In areas with these characteristics, traditional morphometric approaches for predicting process domains, such as area‐slope plots, can be augmented by considering other predictors measured from high resolution lidar‐derived digital elevation models (DEMs). Ordinal logistic regression was used to model the distribution of hillslope, swale, colluvial channel, and fluvial channel domains, as identified during field surveys. The study area was a glaciated region of the Rocky Mountain foothills with a complex lithostructural setting. Relationships between domains and a suite of geographic information system–derived descriptors were explored. Predictors included profile anomalies measured at the reach and basin scale using a normalized stream length–gradient (SL/k) index. Drainage area was the dominant factor controlling domains. A model with area as the only predictor was 82% accurate. Reach slope relations were not consistent. A model that also included lithology and basin‐scale SL/k index variation was 87% accurate. Domain transitions had larger area thresholds in basins with resistant conglomerate versus sandstone or shale formations and where SL/k index was more variable along a profile. In a restricted model of hillslope, swale, and colluvial channel domains, profile curvature measured over 100 m was also related to domain occurrence. A model for regional‐scale mapping applications with six additional predictors was 95% accurate. The results showed that ordinal logistic regression can be used to predict and map process domains in regions with complex physiography using descriptors measured from high ‐resolution DEMs.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".