Bibliographic record
Abstract
In studies of climate change impacts on water resources systems, detailed information on spatial and temporal distribution of climatic variables is often required. Traditionally, this is achieved by downscaling outputs from Global Circulation Models (GCMs). The difficulty in this approach is that GCMs have spatial scales that are incompatible with river basin scales. To circumvent this downfall, an inverse (or a bottom up) approach is able to transform small scale hydrologic exposures into meteorological conditions, and thus link them to large scale GCMs. This paper develops a synthetic storm model (used in the inverse approach) for simulating rainfall events under scenarios of future climate. A methodology is summarized that spatially distributes storms, and includes parameters for storm location, spatial extent, rainfall intensity as a function of distance, maximum amount of rainfall at the storm center, as well as a random component that perturbs the distribution. The temporal storm distribution uses mass distribution functions readily available in the literature. The storm model parameters are regarded as time invariant based on the assumption that the change in processes represented by the parameters is small in comparison with the changes affecting the climate. This assumption is discussed. The model is applied in the Upper Thames River basin, located in south-western Ontario, and some of the results presented.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.007 | 0.001 |
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".