Quantifying terrain controls on runoff retention and routing in the Northern Prairies
Bibliographic record
Abstract
Abstract The role of hummocky terrain in governing runoff routing and focussing groundwater recharge in the Northern Prairies of North America is widely recognised. However, most hydrological studies in the region have not effectively utilised information on the surficial geology and associated landforms in large‐scale hydrological characterization. The present study uses an automated digital elevation model (DEM) analysis of a 6500‐km2area in the Northern Prairies to quantify hydrologically relevant terrain parameters for the common types of terrains in the prairies with different surficial deposits widespread in the prairies, namely, moraines and glaciolacustrine deposits. Runoff retention (and storage) capacity within depressions varies greatly between different surficial deposits and is comparable in magnitude with a typical amount of seasonal snowmelt runoff generation. The terrain constraint on potential runoff retention varies from a few millimetres in areas classified as moraine to tens of millimetres in areas classified as stagnant ice moraine deposits. Fluted moraine and glaciolacustrine deposits have intermediate storage capacity values. The study also identified the probability density function describing a number of immediate upstream neighbours for each depression in a fill‐and‐spill network. A relationship between depression parameters and surficial deposits, as well as identified depression network structure, allows parametrisation of hydrologic models outside of the high‐resolution DEM coverage, which can still account for terrain variation in the Prairies.
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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 | 0.000 | 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".