Ontario Lake-River Routing Product version 1.0
Bibliographic record
Abstract
Thank you for your interest in our lake-river routing product. Please go to this website to download and learn more about the Routing product and BasinMaker. In your publication using the version v1.0 of the routing product, please cite the following paper: BasinMaker: a GIS toolbox for distributed watershed delineation of complex lake and river routing networks. Han, M., H. Shen, B. A. Tolson, J. R. Craig, J. Mai, S. Lin, N. Basu, F. Awol, submitted April 2021 to Environmental Modelling and Software. (But please also check BasinMaker website where you downloaded this for the most up to date citation) Note that version 1.0 of this product covered only Canada and used a different DEM and is described in the following paper: Han, M., J. Mai, B. A. Tolson, J. R. Craig, E. Gaborit, H. Liu, K. Lee, Subwatershed-based lake and river routing products for hydrologic and land surface models applied over Canada, Canadian Water Resources Journal, 45(3), doi.org/10.1080/07011784.2020.1772116. The lake-river routing product provides a routing structure (which here refers to both the topology of the stream network and the contributing areas to individual lakes and stream reaches), to correctly represent lakes and be easily customized based on various user requirements. BasinMaker, which is a GIS toolbox to delineate watersheds with lakes, was used to develop this routing product. In this routing product, each lake is represented by a lake catchment. A lake catchment is defined by the following rules:1) The extent of the lake catchment will fully cover the lake; 2) the outlet of the lake catchment is the same as the outlet of the lake; 3) each lake’s inlets are treated as a catchment outlet. In this way, both inflow and outflow of each lake can be explicitly simulated by hydrologic routing models. Support for BasinMaker and the Ontario lake-river routing product development came from the Ontario Ministry of Northern Development, Mines, Natural Resources and Forestry.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.316 | 0.250 |
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".