Hydrologic outputs generated over the Great Lakes with a calibrated version of the GEM-Hydro model
Bibliographic record
Abstract
This dataset contains outputs from a calibrated version of the GEM-Hydro model developed at Environment and Climate Change Canada (ECCC) and is available on the Federated Research Data Repository. The dataset covers the basins of the Laurentian Great Lakes and the Ottawa River and extends over the period 2001-2018. The data consist of all variables (hourly fluxes and state variables) related to the water balance of GEM-Hydro's land-surface scheme (including precipitation, surface and sub-surface runoff, drainage, evaporation, snow water equivalent, soil moisture…) and mean daily streamflow at 212 gauge locations. These outputs were simulated with a calibrated version of the GEM-Hydro model that was run in open-loop mode (no assimilation) and driven with atmospheric forcings coming from ECCC's Canadian Surface Reanalysis version 2.1. GEM-Hydro achieves satisfactory simulations of various hydrologic variables when compared to reference datasets. This dataset can be used for example to drive any routing model, compute climatologies or statistics for different hydrologic variables and study their variability as a function of the local geo-morphology, etc.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".