Influence of spatial resolution on the distributed surface routing response of the des Anglais river basin (Canada)
Bibliographic record
Abstract
Digital elevation models (DEMs) at different resolutions (180, 360, and 720\nm) are used to examine the impact of different levels of landscape representation on the\nhydrological response of the des Anglais river basin (Canada). Frequency distributions\nof local slope, plan curvature, and drainage area are calculated for each grid size resolution. This landscape analysis reveals that DEM grid size significantly affects computed\ntopographic attributes which in turn explain some of the differences in the hydrological\nsimulations. The investigation is carried out by analyzing the main hydrograph features\n(peak flow, time to peak, and total volume) at the main outlet of the catchment over-3-\nyear simulation period. The simulation results, generated with the surface routing module\nof a coupled surface–subsurface model, indicate that time to peak decreases as grid resolution is coarsened due to a decrease in flow path lengths, that peak flows increase as\ngrid resolution is refined due to an increase in local slopes, and that the simulated runoff\nvolumes increase at coarser grid resolution due to the aggregation of cells at the border\nof the catchment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.000 |
| 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".