Karakterisasi Nukleotida Daerah Ekson 5 dan 6 Gen LDLR Penduduk Papua
Bibliographic record
Abstract
Keragaman suku di Papua berpotensi menghasilkan keragaman genetik. Gen LDLR adalah gen pengkode protein reseptor LDL (LDL-R) yang berperan sangat penting dalam homeostasis kolesterol. Gen LDLR terdiri dari 18 ekson dan 17 intron yang membentang sepanjang 45 kilo basa (kb). Daerah ekson 5 dan 6 merupakan bagian struktural yang penting dalam menyandi asam amino penyusun daerah pengikat ligan yang memediasi interaksi antara reseptor dan lipoprotein yang mengandung Apo B-100 atau Apo E. Penelitian ini bertujuan mengkarakterisasi nukleotida pada daerah ekson 5 dan 6 gen LDLR penduduk Papua dengan asal yang berbeda. Amplifikasi gen target dilakukan menggunakan metode reaksi berantai polimerase (PCR) lalu disekuensing untuk mengetahui urutan basa nukleotida. Hasil karakterisasi menunjukkan bahwa karakter nukleotida daerah ekson 5 dan 6 gen LDLR dari 9 mahasiswa UNIPA asal Papua adalah identik karena memiliki jumlah nukleotida dan susunan nukleotida yang sama. Jumlah nukleotida untuk daerah ekson 5 sebesar 123 pb, terdiri dari T=22,0%, C=24,4%, G=30,9%, A=22,8%, A+T=44,8% dan C+G=55,3%. Untuk daerah ekson 6 dengan jumlah nukleotida sebesar 123 pb, komposisi nukleotidanya yakni T=16,3%, C=26,8%, G=27,6%, A=29,3%, A+T=45,6% dan C+G=54,4%. Hasil ini menunjukkan bahwa kedua daerah tersebut adalah lestari.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".