Optimalisasi Pemenuhan Asupan Gizi Terpadu Dalam Meningkatkan Kualitas Sumber Daya Manusia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Investasi pemerintah dalam kesehatan masyarakat berperan penting dalam meningkatkan produktivitas jangka panjang dan kualitas sumber daya manusia. Tingginya prevalensi stunting di Indonesia menunjukkan adanya tantangan serius, terutama terkait kekurangan energi dan anemia pada anak serta ibu hamil sebagai faktor utama. Melalui pendekatan kuantitatif yang memanfaatkan berbagai sumber data dan literatur relevan, studi ini merumuskan strategi intervensi gizi yang lebih tepat sasaran untuk percepatan penurunan stunting. Temuan menunjukkan bahwa periode emas 1.000 Hari Pertama Kehidupan (HPK) merupakan fase paling efektif untuk memperbaiki kondisi gizi anak. Namun, beberapa program intervensi, termasuk program makan bergizi gratis, belum sepenuhnya memprioritaskan balita dan ibu hamil. Karena itu, diperlukan kebijakan pemenuhan gizi terpadu yang menyinergikan berbagai program yang ada dan berfokus pada kelompok rentan. Dokumen ini diharapkan menjadi kontribusi bagi perumusan kebijakan berbasis bukti dalam upaya percepatan penurunan stunting dan peningkatan kualitas sumber daya manusia di Indonesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it