Spatial Patterns of Pulmonary Tuberculosis Analysing Rainfall Patterns in Visual Formation
Bibliographic record
Abstract
Management sustainability related tuberculosis patient treatment was limited. Tuberculosis analysis was still in the form of data aggregation. This is cross sectional survey using geographical information system, analyzed by descriptive methods, the sample included 162 pulmonary tuberrculosis patient in 2014. The variables were pulmonary tuberrculosis patients and isohyet data. Mycrobacterium tuberculosis will be survive and multiply during rainy season. Rainfall data was an increasing pattern from first quarter to fourth quarter in 2014, however data in 2011, 2012 and 2013, which each quarter was largely experiencing sustained increase and decline. Pulmonary tuberrculosis patients were most prevalent in 2014. It was increase in the rainy season. The most high rainfall intensity (> 2400 mm) in east of Lendah and western of Kokap areas, while the lowest intensity (< 1500 mm) in east of Nanggulan, in the south of Panjatan and Galur areas. It was mostly located in areas with high rainfall intensity (2200 - 2400 mm) which spreads and stretches in Sentolo, Wates, and Panjatan areas. Pulmonary tuberrculosis occurred over the rainy season. Spatial pattern distribution of pulmonary tuberrculosis patients in high rainfall intensity spreads and stretches from east to west areas. Active case monitoring program should be performed by tuberculosis program that concerned in areas of high rainfall intensity.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".