COMPREENSÃO DE MUDANÇAS CLIMÁTICAS REGIONAIS ATRAVÉS DA APLICAÇÃO DE TRÊS MÉTODOS ESTATÍSTICOS
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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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".