Performance of GIS interfaces in Watershed Delineation and Stream Network Generation from DEM
Bibliographic record
Abstract
The Geographic Information System (GIS) tool is efficient in delineating drainage area and generating surface drainage network. Watershed delineation and natural drainage network generation from Digital Elevation Models (DEMs) are the preliminary steps for watershed prioritization, integrated watershed management (lWM) and sustainable development of natural resources within the watershed. In the present study, recently developed GIS supported interfaces within ArcGIS 8.x and Arc View 3.0 software were applied to accomplish these tasks. The DEM of Cowansvile region, Quebec, Canada was used in the present study for delineation of watersheds. The interfaces performed a sequence of activities such as filling the sinks, estimating the flow direction, flow accumulation and stream links to arrive at delineation of watershed from DEMs and generation of drainage networks. In the present study, the interfaces viz. Watershed and Stream Delineation Tool (WSDT), Terrain analysis using DEMs (TauDEM), Watershed Morphology Estimation Tool (WMET) used within ArcGIS 8.x environment and Centre for Research in Water Resources Pre-Processor (CRWR Pre-Pro) interfaces within ArcView 3.x environment were used. It was reveled that the interfaces within the ArcGIS 8.x performed better than that with the Arc View 3.x environment in terms of processing time and accuracy;
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".