Analisa Kebutuhan Nutrisi Balita Wilayah Cilacap (Ditinjau Dari Aspek Imunologi)
Bibliographic record
Abstract
Nutrisi menjadi salah satu faktor penentu sistem kekebalan tubuh atau yang dikenal dengan imunonutrisi. Penelitian yang dilakukan merupakan penelitian menggunakan desain penelitian deskriptif kuantitatif dengan metode penelitian single case riset. penelitian ini akan dilakukan di posyandu di Cialcap selama 2 bulan dengan usia 6-24 bulan. Hasil penelitian didapatkan data sebanyak 39 responden dengan metode kuesioner. Variable yang diamati adalah ausupan Karbohidrat, Lemak, Protein, Buah, Sayur. MPASI, ASI. Diperoleh data bahwa 97.4% balita sudah diberikan karbohidrat dalam bentuk nasi, sedangkan 1 balita atau 2.6% diberikan karbohidrat dalam bentuk yang lainnya. sebanyak 71.8% balita mendapatkan lemak dari minyak goring, 17.9% dari mentega dan 10.3% belum mendapatkan tambahan lemak. 97.4% mengkonsumsi protein baik nabati dan protein hewani. Sedangkan 2.6% belum mengkonsumsi keduanya. 97.4% mengkonsumsi buah dan sayur. Sedangkan 2.6% belum mengkonsumsi buah dan sayur. 76.9% balita mengkonsumsi ASI eksklusif dan 23.1% konsumsi ASI dan susu formula (kombinasi). konsumsi makananan pendamping asi yang buatan sendiri (homemade) sebesar 71.8%. sedangkan 28.2% mengkonsumsi bubur instan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".