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.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".