Pengaruh Pemberian Ekstrak Kedelai (Glycine max) Terhadap Jumlah Pertumbuhan Folikel Ovarium Mencit (Mus musculus)
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh ekstrak kedelai (Glycine max) terhadap jumlah pertumbuhan folikel ovarium pada mencit (Mus musculus). Sampel terdiri dari 20 ekor mencit yang secara acak dibagi menjadi lima kelompok. Kelompok kontrol (K) tidak diberi ekstrak kedelai. Kelompok P1 diberi ekstrak kedelai dengan dosis 0,05 mg/kgBB. Kelompok P2 diberi ekstrak kedelai dengan dosis 0,010 mg/kgBB. Kelompok P3 diberi ekstrak kedelai dengan dosis 0,015 mg/kgBB. Kelompok P4 diberikan ekstrak kedelai dengan dosis 0,020 mg/kgBB. Ekstrak kedelai diberikan selama 14 hari. Analisis data menggunakan uji One Way ANOVA dan dilanjutkan dengan uji Honestly Significant Difference (HSD). Hasil penelitian menunjukkan kelompok kontrol (K) memiliki perbedaan signifikan dengan kelompok P1, P2, P3 dan P4. Hasil rata-rata jumlah folikel primer tertinggi terdapat pada perlakuan 4 (P4) dengan dosis 0,020 mg/kgBB dan jumlah rata-rata folikel sekunder tertinggi pada perlakuan 3 (P3) dengan dosis 0,015 mg/kgBB. Penelitian ini dapat disimpulkan bahwa ekstrak kedelai yang diberikan pada mencit betina dapat meningkatkan jumlah pertumbuhan folikel ovarium dan jumlah rata-rata folikel tertinggi adalah pada perlakuan 3 (P3) dengan dosis 0,015 mg/kgBB.
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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) | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".