Four-kilometer long-term regional hydroclimate reanalysis over the conterminous United States (CONUS)
Bibliographic record
Abstract
CONUS404 is a unique, high-resolution hydro-climate dataset appropriate for forcing hydrological models and conducting meteorological analysis over the conterminous United States. CONUS404, so named because it covers the CONterminous United States for over 40 years at 4 km resolution, was produced by the Weather Research and Forecasting (WRF) model simulations run by NCAR as part of a collaboration with the USGS Water Mission Area. The CONUS404 includes 42 years of data (water years 1980-2021) and the spatial domain [https://osdata.gdex.ucar.edu/web/datasets/d559000/docs/CONUS404_BAMS_fig01.png] extends beyond the CONUS into Canada and Mexico, thereby capturing transboundary river basins and covering all contributing areas for CONUS surface waters.The CONUS404 dataset, produced using WRF version 3.9.1.1, is the successor to the CONUS1 dataset in d612000 [https://gdex.ucar.edu/datasets/d612000/] (Liu, et al. 2017) with improved representation of weather and climate conditions in the central United States due to the addition of a shallow groundwater module and several other improvements in the NOAH Multi-parameterization land surface model. It also uses a more up-to-date and higher-resolution reanalysis dataset (ERA5) as input and covers a longer period than CONUS1.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".