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
There is a geopolitical concept of Tabgach and northern in historiography. Until the first quarter of the 10th century the of Turkic origin could dominate in northern China since IV. It's basically the Xiongnu (Huns), Xianbei (Toba dynasty), SE (SI Jie; Sak), tuczjuje dynasty Shabolio (Yshbara), Tans Chateau dynasty (Tang). From the above mentioned and Chinese Jin was formed a powerful public entity Tabgach (Taugach, Tavgast, Tamgach) on the vast territory of northern China. The ethno-cultural tradition and language of the of Xianbei, Shabolio, Chateou dominated in a country. The important thing is that these historical ethnonyms, patronims can be identified with similar names (eponyms) in the epic Manas. It was not stated in the epic about ethnonyms (and patronims, titles), Xiongnu, Xianbei ASE, Shad, Shehu, Château, Shabolio, etc., because they are all Chinese written identification (and fixing) Turkic (Kyrgyz) names (childbirth, dynasty). EPIC titles (ethnonyms, toponyms, eponims) precede and truthful, as the first and subsequent storytellers-manaschi artistically sang historical events the grandiose era where you couldn't lie or distort in front of thousands of critical audiences. From the first quarter of the 10th century the dynasty of Liao-Tai-Zu dominated Tabgach. Apparently, along with the collapse of the historic Kyrgyz Empire (the Middle X.) (accordingly, the epic Kyrgyz State era the Manas trilogy) disintegrated and Tabgach five northern tribes of China. Ethnonyms (and patronims) Tabgach could be identified with epic etnonims (and eponyms), historical Kidans with epic Kara-Chinese from the epic Manas.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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