The effects of landscape discretization and snowpack initialization on the performance of a semi-distributed runoff model for an
Bibliographic record
Abstract
Detailed snow depth data was collected using UAVs during the spring of 2019 and summer of 2020 to create a snow cover map of a 1.2 km 2 sub-basin of the Apex watershed, Iqaluit, Nunavut.A semi-distributed hydrological model was built using the Raven hydrological modelling framework to test how the representation of the landscape's snow cover variability impacts model performance.Four hydrological response unit (HRU) discretization schemes were examined.The snow cover map was used to delineate HRUs using five and ten clusters of snow depth, which were compared with an average basin and a land cover discretization scheme.The five-cluster scheme outperformed all the schemes by better simulating end-of-melt (late June) streamflow; cumulative evapotranspiration (ET) and estimated snow free date.Results demonstrate that HRUs derived from snow depth clusters outperform the current methods commonly used.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".