Bibliographic record
Abstract
Climate change studies are crucial to assist decision-makers in understanding future risks and planning adequate adaptation measures. In general, Global/Regional Climate Models (GCMs/RCMs) achieve coarse resolutions, and are thus unable to provide sufficient information to conduct local climate assessments. Downscaling, defined as a method that derives local to regional-scale (10 to 100 km) information from larger-scale models or data analyses, is used to address this deficiency. In this thesis, a particular downscaling technique, known as the Quantile-Quantile transformation, was used to adjust the statistical distribution of RCM variables to match the statistical distribution of the observed variables generated by two RCMs: the Canadian Regional Climate Model version 3.7.1 and the ARPEGE model, on the historical period (1961-2001). The analyses presented in this study were applied to daily precipitation as well as maximum and minimum temperatures in the South Nation watershed in Eastern Ontario, Canada. The two-sample Kolmogorov–Smirnov test indicated that the Quantile-Quantile transformation improved the shape of the PDF of RCM-simulated
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".