Data in a framework to quantify the uncertainty contribution of GCMs over multiple sources in hydrological impacts of climate change
Bibliographic record
Abstract
This dataset includes the daily hydrological simulations under climate change scenarios for the Manic-5 watershed in Canada and the Xiangjiang watershed in China. The format of all files is binary MATLAB file (.mat). The hydrological simulations for each watershed were obtained through an impact modeling chain, which comprises multiple options for each step. To be specific, three greenhouse gas emission scenarios (RCP2.6, RCP4.5 and RCP 8.5), 22 global climate models, 6 downscaling techniques, 5 hydrological models, and 5 sets of parameters for each hydrological model are included. Thus, there are overall 9900 hydrological simulations over the reference (1970-1999) and future (2070-2099) periods for each watershed. This dataset can be used to evaluate the overall uncertainty and uncertainty components in the modeling of hydrological impacts from climate change.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.010 |
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; both teacher heads agree on what is shown here.
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".