The giving effects of biscuits and tempeh-based flour cakes as supplemetary feeding towards improvements in body weight and height in children suffering malnutrition 2015
Bibliographic record
Abstract
Background: Nutritional problem in children is considered as a major issue and one that should receive priority in treatment is concerning to malnutrition. Malnutrition in children occurs due to insufficiency in energy and protein. Energy and protein are required in supporting rapid growth in children.Objective: to learn the giving effects of biscuits and tempeh-based flour cakes as supplementary feeding towards improvements in body weight and height of children under five suffering malnutrition in one of Public Health Center working area, northern region of Kediri Municipality.Methods: This study used a Randomized Control Triall Design, towards groups of children under five by providing tempeh-based flour cakes for supplementary feeding in the treatment group and by providing biscuits for supplementary feeding in control group. Samples were as many as 30 children under five and the data were being analyzed using peason and pre-post differences analysis was done using paired samples T-testResult: There was significant difference between body weight and body height after provision of providing biscuits and tempeh-based flour cakes for supplementary feeding with p = 0.001 (p <α) for the treatment group and p = 0.001 (p <α) for the control group.Conclution: Providing and tempeh-based flour cakes in the treatment group for 30-days gave influence on increasing body weight and height.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".