NAC<sup>2</sup>H: The North American Climate Change and Hydroclimatology Data Set
Bibliographic record
Abstract
Abstract A data set containing hydrometeorological and hydroclimatological data for 3,540 watersheds in North America is described. The data set contains four main parts: (a) observed hydrometeorological data including daily streamflow observations, precipitation, minimum temperature, and maximum temperature; (b) 20 bias‐corrected climate model projections for two Representative Concentration Pathway (RCP) scenarios and five bias correction methods; (c) hydrological model calibration parameters and simulated streamflow for 4 hydrological models, 2 objective functions, and 10 calibration parameter sets for the reference period; and (d) hydrological simulations for each of the combinations of the abovementioned elements of the climate change impact study chain, for a total of 16,000 combinations. The data set also contains simulations and bias‐corrected climate for 30‐year horizons corresponding to 1.5°C and 2°C temperature increases for a subset of the climate models, for an additional 8,000 combinations. All simulations in the reference period are also provided. Fifty‐one precomputed hydrological indices are made available for each simulation. Overall, 2.89 × 1012 years of simulations are classified, analyzed, compressed, and made available for all researchers. This data set can be used to evaluate the uncertainty of various components in the impact study chain, to establish relationships between catchment properties and hydrological response to climate change, and to evaluate the spatial distribution of hydrological change according to a multitude of hydrological indices.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.020 |
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".