Bibliographic record
Abstract
This study investigates the control of atmospheric circulation patterns on rainfall incidence in Spain. The main objective of the research is to evaluate a range of statistical synoptic approaches with the aim of identifying the scheme that best models circulation to association. Spatial patterns of rainfall in Spain are first investigated using Principal Components Analysis and Cluster Analysis. Distinct precipitation affinity groups emerge that display covariant rainfall behaviour and reflect differnces in latitude, the influence of topography and distance from the synoptic feature responsible for rainfall. The method allows seasonal redefinition of boundaries and the investigation of the effect of climate change. In total 24 synoptic models are investigated. The best performing models (a daily weather type model and a monthly airflow index model) use standardized data and the 500hPa contour surface. Some of the problems associated with non-stationarity are attempted by modifying models using kinematic information. Adjustments to the models (inclusion of frontal information and stochastic modelling) can improve results on a sub-regional scale. Effective models are then used to empirically downscale from General Circulation Model (GCM) scenarios obtained from the Canadian Centre for Climate Modelling and Analysis. The downscaling procedure is of limited use due to errors in GCM output but results suggest strongly increasing anticyclonicity in the Iberian area and a decrease in rainfall in many areas. There are uncertainties associated with regional scale climate change estimation using current empirical methods, nevertheless as GCM output inevitably becomes more accurate the scope for detailed regional assessment will improve.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".