Bibliographic record
Abstract
1. Mempersiapkan bahan pakaian dari kulit kayu. 2. Pengolahan kulit kayu. 3. Alat pemukulan. 4. Memukuli kulit kayu. 5. Penyelesaian fuya. 6. Melukis fuya. 7. Anyaman (moena, mancua). 8. Anyaman dengan potongan daun dan tangkai tanaman. 9. Bambu dan rotan sebagai bahan anyaman. 10. Pola anyaman dan gambar anyaman. 11. Rotan sebagai bahan anyaman. 12. Bahan anyaman lainnya. 13. Keranjang dari pelepah dahan sagu. 14. Tembikar sebagai kerajinan tangan. 15. Membentuk dan membakar pot. 16. Pelantikan panci masak. 17. Gerabah buatan Toraja. 18. Pembuatan garam oleh orang Toraja. 19. Menguapnya air laut. 20. Garam asli. 21. Persiapan gula. Perapian (balombo). 22. Sari aren dan pengentalnya. 23. Gula Aren. 24. Menempa besi. Menggali bijih. 25. Pandai besi dan Bengkel. 26. Tungku, Ububan, Landasan dan Peralatan lainnya. 27. Barang besi Toraja. 28. Pesta di bengkel (mopatawi). 29. Penggunaan besi secara ajaib. 30. Pengecoran tembaga. 31. Pembuatan perahu
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 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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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; both teacher heads agree on what is shown here.
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".