Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment
Bibliographic record
Abstract
Appropriate representation of landscape heterogeneity at small to medium scales is a central issue for hydrological modelling. Two main hydrological modelling approaches, deductive and inductive, are generally applied. Here, snow-cover ablation and basin snowmelt runoff are evaluated using a combined modelling approach that includes the incorporation of detailed process understanding along with information gained from observations of basin-wide streamflow phenomena. The study site is Granger Basin, a small sub-arctic basin in the mountains of the Yukon Territory, Canada. The analysis is based on the comparison between basin-aggregated and distributed landscape representations. Results show that the distributed model based on “hydrological response” landscape units best describes the observed magnitudes of both snow-cover ablation and basin runoff, whereas the aggregated approach fails to represent the differential snowmelt rates and to describe both runoff volumes and dynamics when discontinuous snowmelt events occur.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".