Bibliographic record
Abstract
1. Penampilan Umum Sulawesi Tengah. 2. Toraja yang berbahasa Bare’e (Kelompok Toraja Timur). 3. Apakah orang Toraja setibanya di daerah ini mengusir suku-suku lain? DAS Poso. 4. Lembah Poso. 5. Danau Poso. 6. Tepi barat dan timur Danau. 7. Sisi selatan Danau. 8. Asal Usul Danau Poso. 9. Angin dan fauna Danau. 10. Desa-desa yang tenggelam. 11. Legenda terhubung dengan Danau. 12. Pamona. Batu perpisahan. 13. Tamungku-mbalugai. Kedatangan Lasaeo di bumi. 14. Pertengkaran Lasaeo dengan istrinya. 15. Lasaeo kembali ke langit. 16. Koyoe, putra Lasaeo. 17. Ndori, cucu Lasaeo. 18. Perjalanan Ndori ke Waibunta. 19. Kunjungan Lasaeo (Sawerigading) ke Pamona. Pembagian Warisan. 20. Lasaeo sebagai pahlawan budaya. 21. Aliran Sungai Poso. 22. Dataran Batu- noncu. 23. Jalan-jalan di sepanjang Sungai Poso. 24. Daerah pegunungan Tomasa. 25. Penghuni Danau. To Lamusa. 26. To Pu'u-mboto. 27. To Bancea. 28. To Longkea. 29. To Wingke-mposo. 30. To Lage. 31. To Kadombuku. 32. Sejarah To Payapi. 33. Cerita tentang To Payapi. 34. Keruntuhannya To Payapi. 35. To Pebato sebagai pengganti To Payapi. 36. Pemerasan To Napu. 37. Pendirian Mapane. 38. Kantor administrasi Poso. DAS Laa 39. DAS Laa. 40. To Onda'e dan desanya. 41. Runtuhnya Wawo-onda'e. 42. Sejarah Wawo-ntolo. 43. Karaja Onda'e. 44. Jajaran gunung timur dan lembah Yaentu. 45. The To Onda'e sebagai To Kampenaar (Orang kikir). 46. Sejarah To Langgeani. 47. Daerah Palande. 48. Tawualongi. 49. Daerah Pakambia. 50. Gerakan religio-politik “meyapi”. 51. Penduduk dataran Laa. DAS Kalaena 52. Aliran Kalaena dan anak-anak sungainya. 53. Penghuni lembah Kalaena. Daerah Tojo 54. Daerah Tojo. 55. Toraja Tojo. 56. To Ampana dan To Wana. 57. Raja-Raja Tojo. 58. Tanda Kerajaan Tojo.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.100 | 0.049 |
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".