A web-based watershed delineation tool and its application to delineate 24,000 watersheds across Canada
Bibliographic record
Abstract
Watershed delineation is the identification of the boundary of a drainage basin, representing the contributing area for a specific outlet. This application of hydrography is essential in the analysis of watershed behaviour and has historically been performed manually. The automation of delineation provides faster and more consistent results which can be more accurate and reproducible definitions of borders compared to the results of the manual delineation. There is a wide range of software and tools capable of performing watershed delineation automatically; all generally following the same steps – utilizing conditioned DEMs to create flow direction and accumulation rasters used in addition to a specified pour point that defines the extent of contributing area desired. These different tools and their results have been explored, addressing their similarities, contrasts, and complications. Using this analysis, selected methods have been included in a web application for watershed delineation for users to either delineate individual points selected on a web map or upload lists of points of interest The automatically delineated watersheds are then made available for download. One tool has been deemed most applicable and has been used to delineate more than 24,000 watersheds across Canada successfully. The presentation will include (1) the results of the comparison of the various tools tested, (2) a demonstration of the webtool as well as (3) the presenting the results of the large scale delineation task across Canada.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".