Climate change impacts on hydrometeorological variables using a bias correction method: The lake Karla watershed case
Bibliographic record
Abstract
This paper is dealing with the assessment of climate change scenarios on selected meteorological variables at the Lake Karla watershed, central Greece and it is a contribution to the “HYDROMENTOR” research project. A statistical downscaling method has been employed to estimate monthly precipitation and temperature time series for future climate periods. The outputs of the Canadian Centre for Climate Modeling Analysis Global Circulation Model CGCM3 were applied for three socioeconomic scenarios, namely SRES B1, SRES A1B and SRES A2 for the assessment of climate change impact on precipitation and temperature. The methodology has been developed for the historical period 1980-2000 and applied for two future periods 2030–2050 and 2080–2100. Historical and future periods were compared for monthly and annual basis using various statistical criteria such as the mean and standard deviation. Results showed that climate change will have minor impacts on precipitation and even smaller impacts on temperature.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".