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.
Bibliographic record
Abstract
1. Bumi. Gempa bumi. 2. Langit. 3. Perjalanan matahari. 4. Terbit dan terbenamnya matahari. 5. Matahari tidak disembah. 6. Ayam jantan memanggil matahari. 7. Anak-anak matahari dan bulan. 8. Gerhana matahari. 9. Tanda-tanda tentang matahari dan mimpi tentang matahari. 10. Matahari sebagai alat transportasi. 11. Apa yang diceritakan dengan bulan. 12. Pria di bulan. 13. Tanda-tanda di bulan. Gerhana bulan. 14. Pemujaan bulan. 15. Cerita tentang bulan. 16. Bintang-bintang dulunya adalah manusia. 17. Segala macam keterangan tentang bintang-bintang. 18. Komet. 19. Bintang kejora dan bintang petang. 20. Rasi bintang "Ayam Jantan ." 21. Cerita tentang Indo i Rambue. 22. Manu-tadia mengalami patah kaki. 23. Kisah tentang Bora-umonto dan Manu-mbaraka. 24. Penyembahan bintang. 25. Bagaimana hujan terjadi. 26. Jiwa orang mati dan dewa memiliki kekuatan atas hujan. 27. Hujan dan air membawa kekuatan kehidupan bagi manusia. 28. Hujan ditambah dengan sinar matahari. 29. Larangan saat hujan. 30. Apa yang menyebabkan hujan. 31. Pengaruh hewan dan tumbuhan terhadap hujan. Pertanda hujan dan kekeringan. 32. Apa yang membangkitkan badai. 33. Sarana yang digunakan seseorang untuk mencegah badai. 34. Guntur dan kilat. 35. Segala macam kepercayaan sehubungan dengan munculnya pelangi. 36. Pelangi sebagai jalan menuju langit.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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 it