The Impact of Climate Modes on Summer Temperature and Precipitation of Darwin, Australia, 1870-2011
Bibliographic record
Abstract
Monthly mean summer (DJF) temperature and precipitation from Global Historical Climate Network (GHCN-V3) for the period of 1870-2011, are analyzed to assess the role of teleconnections on climate of Darwin, Australia. Indices of El Nino-Southern Oscillation (ENSO), Antarctic Oscillation (AAO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), Arctic Oscillation (AO), and Pacific North American Oscillation (PNA) are extracted from monthly means and compared with climatic data of Darwin. Most of these climate modes are shown to have a strong influence on the monthly mean summer temperature and precipitation. ENSO is shown to have a positive relationship with the amount of precipitation received and a negative relationship with the temperature. Where an El Nino event produces warmer drier conditions and a La Nina event produces colder wetter conditions. The AAO is shown to cause cold and dry conditions during the positive phase and warm and wet conditions during the negative phase. The PDO is shown to cause El Nino like condition during the positive phase causing warmer, drier weather, and La Nina like conditions during the negative phase causing cooler, wetter weather. Through the analysis it is also shown that the NAO, AO, and PNA have little effect on the temperature and precipitation patterns of Darwin.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".