The Influence of the Antarctic Oscillation (AAO) on Cold Waves and Occurrence of Frosts in the State of Santa Catarina, Brazil
Bibliographic record
Abstract
This paper examines the relationship between the Antarctic Oscillation (AAO), cold waves and occurrence of frosts in the state of Santa Catarina, Brazil, during the winter quarter. Research on this topic can assist different spheres of society, such as public health and agriculture, since cold waves can influence and/or aggravate health problems and frosts can inflict economic losses especially in the agricultural sector. For the purpose of this paper, cold wave is considered as the event in which the daily average surface air temperature was at least two standard deviations below the average value of the series on the day and for two consecutive days or more. The data on the average air temperature and frost occurrences are provided by the Company of Agricultural Research and Rural Extension of Santa Catarina/Center for Environmental Information and Hydrometeorology (EPAGRI/CIRAM). The AAO was subjected to statistical analysis using significance tests for the averages (Student’s t-test) and variances (F-test) with a significance level of α = 5%. The results show that cold waves are unevenly distributed in the agroecological zones of Santa Catarina. It is found that the AAO is associated with the occurrence of frosts (in the agroecological zones represented by the municipalities of Itajaí and São José) in the state of Santa Catarina.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".