Influence of landscape aggregation in modelling snow-cover ablation and snowmelt runoff in a sub-arctic mountainous environment
Why this work is in the frame
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Bibliographic record
Abstract
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 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 it