COMPREENSÃO DE MUDANÇAS CLIMÁTICAS REGIONAIS ATRAVÉS DA APLICAÇÃO DE TRÊS MÉTODOS ESTATÍSTICOS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study assesses the use of historical climate data as well as traditional and non-traditional statistical methods to understand climate change at a regional level. Three different approaches were considered: i) general evaluation of climate data evolution, including comparison between two periods (early and late years); ii) trend analysis; and iii) cluster analysis. Daily data of rainfall and snowfall were obtained from the Sudbury Airport weather station (Canada) from January 1956 to December 2010 (55 full years). The comparison between periods revealed that annual rainfall is increasing in the studied location, being 12% higher in recent years. Trend analysis and cluster analysis showed that these increasing annual trends were not uniform throughout the year, occurring mainly in winter and spring. On the other hand, decreases in summer rainfall were detected by cluster analysis only. According to cluster analysis results, summers are becoming drier in the location, although overall, years are becoming wetter. Regarding snowfall, there was no difference between the two periods compared and trend analysis detected no significant trends. However, cluster analysis showed clear changes during the main months of snowfall (December, January and February), indicating that climate in the location is changing towards late winters regarding snowfall. Thus, the results demonstrate that inclusion of simple methods such as cluster analysis, combined with more traditional statistical methods, can contribute to a better understanding of climate change.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it