Varietal Diversity and Nutritional Analysis of Sago Starch (Metroxylon Sago Rottb) in Kainui, Papua
Bibliographic record
Abstract
Sago as a source of starch has an important role as a food ingredient.The sago starch used by Papuan people consists of various varieties and so far, the public does not understand the contents of each sago variety.The aim of this research is to determine the diversity of sago varieties and determine the sago starch content in several sago varieties in Kainui.This research used a sample plot method measuring 25 m × 25 m with 16 plots which functioned to obtain data on the distribution of sago ready to be harvested.The harvested sago tree is measured for its length, diameter, base, middle and tip to 1 meter, then skinned and shredded.The sago pith is grated, squeezed and the starch is extracted then put into an aqua bottle.Various protein contents were calculated using the AOAC 2006 formula.The results of the research showed that five varieties of sago were found with different growth and varietal diversity.The results of the analysis show that the best quality of sago starch is the sago amin variety with a value of 92.3%, the highest water content is found in the sago amin variety, the lowest ash content is in the sago manoari variety 0.07%.The highest starch production was in the sago hawar variety at 99.34%, while the highest amylose content was found in the sago hawar variety with a value of 25.91%.This study provides recommendations to sago farmers in Papua about five sago varieties in Kainui with the best nutritional content in each sago variety, namely the Amin sago variety and also to the Kainui Regional Government as a basis for policy making for future sago starch processing.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".