Climatic variation of the structure of maximum daily temperatures in Spain: A combined statistical and computational intelligence approach.
Bibliographic record
Abstract
Two blocks (1904-1921 and 1990-2007) of daily maximum temperature data from seventeen Spanish meteorological stations exhibit a multimodal empirical distribution function (EDF). Most of the stations show important differences in their EDF for each one of the considered periods of time, a fact that reveals the complexity of climatic changes within the accepted general warming trend of the Iberian Peninsula. As a tentative approach to understand the underlying structure of data, each EDF has been decomposed on two normal distributed functions. The parameters describing these functions for each station and for each time period have been space-optimized and visualized using classical optimization and genetic programming. The changes in the geographical distribution of the classes derived from the analysis point towards a recent greater role of Mediterranean climates, spreading its influence to the interior of the Peninsula. The general picture, however, is much more complex than a linear warming and a number of stations even show negative trends. This study is considered to be a preliminary methodological exploration of future procedures destined to close the gap between data driven analysis and what models based upon first principles may tell.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".