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KARAKTERISTIK MI TINGGI ANTIOKSIDAN DARI DAUN KELOR (Moringa oleifera L.) DAN DAUN BELUNTAS (Pluchea indica L.)

2022· article· id· W4298013192 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJURNAL REKAYASA DAN MANAJEMEN AGROINDUSTRI · 2022
Typearticle
Languageid
FieldAgricultural and Biological Sciences
TopicMedicinal Plant Research
Canadian institutionsASTER
Fundersnot available
KeywordsPhysicsHorticultureMoringaTraditional medicineBiologyFood scienceMedicine

Abstract

fetched live from OpenAlex

Tanaman kelor (Moringa Oleifera) dan beluntas (Pluchea Indica) mengandung antioksidan yang tinggi sehingga bisa dimanfaatkan sebagai makanan fungsional. Tujuan dari penelitian ini adalah untuk mengetahui karakteristik mi yang dibuat dari eksrak daun kelor dan beluntas. Penelitian ini menggunakan rancangan acak lengkap non faktorial dengan 5 variasi formula pembuatan mi. Paramater yang diamati meliputi analisis warna, antioksidan dan uji sensoris. Data hasil penelitian dianalisis menggunakan uji F pada taraf signifikasi 5% apabila ada beda nyata dilanjutkan dengan uji DMRT?5% dengan bantuan sofware SPSS versi 21. Hasil penelitian menunjukkan bahwa penggunaan daun kelor dalam bentuk bubur dapat meningkatkan kandungan antioksidannya dengan nilai 11,63.%.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.345
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.003
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0040.003
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.042
GPT teacher head0.266
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it