GEM-Hydro gridded simulations for the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E)
Bibliographic record
Abstract
This dataset provides gridded model simulations in NetCDF format over the Lake Erie using the GEM-Hydro model done within the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E). The data are produced with SPS (GEM-Surf + SVS, the surface component of GEM-Hydro) open-loop runs with the SVS calibrated parameters obtained during GRIP-E project. For more information on the model and on calibration methodology, see GEM-Hydro section in Mai et al. 2020 (in prep.). The original model outputs had all variables accumulated for each day. During post-processing all variables have been de-accumulated by subtracting the accumulation of the previous hour from the accumulation of the current hour. Two variables (ALAT and O1) are also only valid over the land tile of each grid cell. Two additional variables (ALAT_full and O1_full) valid now over the whole grid cell have been added for convenience of the users. Domain boundaries (WGS84 system): - lon_min = -85.5, lon_max = -77.94 - lat_min = 40.3, lat_max = 44.26 Resolution of model variables provided: - spatial: ~10km x 10km - temporal: hourly Simulation period: - 01 Jan 2011 - 31 Dec 2014 - 01 Jan 2010 - 31 Dec 2010 (warm-up) Meteorological input data: - RDRS-v1; see Mai et al. 2020 (in prep) Variables available: float PR_0(time, rlat, rlon) ; PR_0:units = "m" ; PR_0:long_name = "Quantity of precipitation (valid over whole grid cell)" ; float AHFL_0(time, rlat, rlon) ; AHFL_0:units = "mm" ; AHFL_0:long_name = "Surface evaporation (valid over whole grid cell)" ; float TRAF_60268832(time, rlat, rlon) ; TRAF_60268832:units = "mm" ; TRAF_60268832:long_name = "Surface runoff (valid over whole grid cell)" ; float ALAT_0(time, rlat, rlon) ; ALAT_0:units = "mm" ; ALAT_0:long_name = "Accumulation of total soil lateral flow (valid over land tile of grid cell)" ; float ALAT_0_full(time, rlat, rlon) ; ALAT_0_full:units = "mm" ; ALAT_0_full:long_name = "Accumulation of total soil lateral flow (valid over whole grid cell)" ; float O1_0(time, rlat, rlon) ; O1_0:units = "mm" ; O1_0:long_name = "Accumulation of base drainage (valid over land tile of grid cell)" ; float O1_0_full(time, rlat, rlon) ; O1_0_full:units = "mm" ; O1_0_full:long_name = "Accumulation of base drainage (valid over whole grid cell)" ; float WT_59868832(time, rlat, rlon) ; WT_59868832:units = "1" ; WT_59868832:long_name = "Fraction of grid cell covered with land" ; =============================================================== These data and model runs have been performed under the Great Lakes Runoff Inter-comparison Project for Lake Erie (GRIP-E) led by Juliane Mai and Bryan Tolson (both University of Waterloo) and funded under the Integrated Modelling Program for Canada (IMPC) within the Global Water Futures program.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".