Pengaruh Konsumsi Ekstrak Daun Katuk Terhadap Kecukupan ASI Pada Ibu Menyusui Di Klaten
Bibliographic record
Abstract
Abstract: Sauropus Androgynus, Breast Milk. Mother breat Milk is good nutrition for baby. Mother breast Milk important for growing baby. To increasing mother breast milk very good consumption food this like : leaf Katu (Sauropus androgynous), leaf Ubi jalar (Ipomoea batatas), leaf kelor (Moringa oleifera), fried corn at all, Composition of leaf katuk is proteins, fats, calcium, phosphor, iron, vitamins A, B, and C. pyrrolidinone, metil pyroglutamate and p-dodesilfenol component minor. Goal research to known relationship consumption leaf katuk with breast milk sufficient, at midwife practice Independent (BPM) on Klaten area. Methods: Pre-Posts with Control Group Design. In the research researcher measure influence intervention at an eksperiment group with comparing group control. Research worked at midwife practice Independent at the January as to Juli 2015. Population in the research is all of the mother breastfeeding at midwife practice Independent (BPM) on Klaten district. Sampling methods is quota sampling with inclusive criterion normally newborn and healthy. Research at January-July 2015. Data analysis performed to describe the variable that will be studied and performed bivariate analyze to the relationship of independent and dependent variable s using the chi-square. Results research is 70 % intervention group more produced milk than control group produced milk enough 30%. Result in statistik Analice chi-square p-value =0,002.Conclusion there was significant relationship consumption extract katuk for sufficient breast milk.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".