BasinMaker 3.0: A GIS toolbox for distributed watershed delineation of complex lake-river routing networks
Bibliographic record
Abstract
Hydrological modelling efforts tend to ignore the impacts of lakes or explicitly simulate the behavior of only the largest lakes in a watershed as deriving information required to explicitly represent thousands of lakes is difficult. We introduce an open-source GIS toolbox (BasinMaker) that can efficiently build vector-based hydrological routing networks including an arbitrary number of rivers and lakes, with attributes (e.g., network topology, subbasin and lake geometry, channel characteristics) that provide the inputs required for hydrological routing models. BasinMaker functionality is demonstrated to build two high-resolution vector-based lake-river routing products each defining a collection of routing networks across large regions: the North American Lake-River Routing and Ontario Lake-River Routing Products. Each includes all lakes over 10 ha identified in the HydroLAKES dataset. BasinMaker is unique in terms of lake representation and is especially helpful for modelers who need to explicitly represent numerous lakes in their watershed simulation models.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.061 | 0.016 |
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".