Model Regresi Data Panel Pada Kasus Infeksi Saluran Pernapasan Akut (ISPA) di Provinsi Nusa Tenggara Timur
Bibliographic record
Abstract
Acute respiratory infection (ARI) is an infectious desease cause by bacteria or viruses that attack the respiratory organs. This research aims to determine the best panel data regression model in the case of the factors that influence the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Response variable used is the number of ARI patients. Independent variables were observed among others, low birth weight, malnutrition, immunization, exclusive breastfeeding, and vitamin A in 22 districts or city in East Nusa Tenggara. The results showed that the Random Effect Models eliminate outlier data on response variable is a model that can describe the influence of independent variables on the number of patients with ARI in East Nusa Tenggara Province from 2014 to 2018. Variables that influence of ARI are malnutrition and exclusive breastfeeding with a coefficient of determination (R) of 9,2%.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".