Проявление полового диморфизма в антропологическом облике народов Северной Евразии
Bibliographic record
Abstract
The article makes a quantity evaluation regarding variations in the absolute value of intersexual distinctions in groups, following a complex of measuring/descriptive racial and diagnostic characters of mans look, representing independent systems which mark sex dimorphism. They attracted a new source base of 70 samplings of Eurasian modern ethnic groups. The author suggests methods of calculating the index of level of sex dimorphism in the group. The index was generalized for a complex of features, with consideration on possible reasons for the registered distribution. Subject to determination being certain specificity of East European, Asian and metis samplings. What is typical for the latter being a considerable increase of sex dimorphism in accordance with length of body, colour of eyes, height of a nose bridge, frequency of a concave back of nose, and prominence of cheek bones. The feminine part of the population tends to better preserving features of the look, typical for infant age. A summary value of sex dimorphism could be considered as an indirect criterion of groups homogeneity. In accordance with a racial composition, the most homogeneous groups being the Podkamenka Evenks, the Vadeyevo Nganasans, the Kineshma Russians, while the most geterogeneous ones being the Kolva Nenets, the Enets, the Inta North Komi, the Kamchatka Russians, the Nazym river Khanti. The value of sex dimorphism practically is not connected with the race.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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; 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".