Southern Oscillation Index and transmission of the Barmah Forest virus infection in Queensland, Australia: Figure 1
Bibliographic record
Abstract
El Niño-Southern Oscillation (ENSO) events are usually accompanied by changes in the trade winds, cloud amounts and rainfall over the tropical Pacific and Australian regions, and seem to be related to many climatic anomalies around the globe.A measure of ENSO is the Southern Oscillation Index (SOI), which is the normalised atmospheric pressure diVerence between Tahiti in the south Pacific and Darwin in northern Australia.The SOI is closely related to variations in temperature and rainfall across the Pacific and in eastern Australia. 1 A positive index (low pressure at Darwin, greater rainfall, higher sea levels) means that the south eastern trade winds feed moisture across the Pacific towards the Australian region.Hence positive SOI values would tend to favour salt marsh mosquito breeding, 2 and as a consequence might impact on the transmission of some mosquito borne diseases such as the Ross River virus infection.3 Barmah Forest virus (BFV) infection, characterised by polyarthritis, myalgia, rash, fever, lethargy and malaise, is caused by an alphavirus, with Aedes and Cules mosquitoes as major vectors and marsupials as suspected host.The incubation period may be 7-9 days, the rash lasts an average of seven days and BFV infection may also lead to chronic illness in some patients.4 It has been regarded as one of the most
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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) | 0.009 | 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".