CANOPEX: A Canadian hydrometeorological watershed database
Bibliographic record
Abstract
Abstract Over the past few years, many international initiatives and collaborations were launched to improve and share knowledge in hydrology research. Large databases allowed finding patterns and relationships across regions and scales. This paper introduces the Canadian model parameter experiment (CANOPEX) database, which is adapted from the US MOPEX project data and methods. The CANOPEX database includes meteorological and hydrometric data as well as watershed boundaries for 698 basins. Two sets of basin‐averaged meteorological data (Maximum and minimum temperature and precipitation) are provided. The first dataset is directly taken from Environment Canada's weather stations whereas the second is extracted from the Natural Resources Canada gridded climate data product. Data are provided in MOPEX and Matlab formats. CANOPEX watersheds are well distributed over Canada, which allows investigating a variety of physiological and climatological conditions. The CANOPEX database can be used in a variety of hydrologic research projects such as climate change impact studies, model comparisons, multi‐modelling, ensemble streamflow prediction and model parameter estimation. CANOPEX could be used to generalize findings to other cold climate catchments as well as assess the robustness of research methodologies and procedures. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".