Taphonomic bias in preservation and representativeness of skeletal samples (a case of Gonur Depe)
Bibliographic record
Abstract
The topic of this article is theoretical and practical issues underlying the problems of representativeness of skeletal samples, as well as age and sex biases in preservation of skeletal remains, and the impact of these fac-tors on paleodemographic reconstructions. The impact of taphonomic bias in preservation on the qualitative and quantitative composition of skeletal sample is discussed on the materials from Gonur Depe — a Bronze Age proto-urban center in Southern Turkmenistan (2500–1500 BC, BMAC — Bactria-Margiana archaeological com-plex, also referred to as Oxus Civilization). The analyzed sample consists of skeletal remains of 500 individuals (215 non-adults, 115 adult males and 170 adult females) excavated between 2008 and 2015. Based on the type of preservation of skeletal remains, their completeness, as well as the preservation index (computed for each individual by dividing the number of long bones present by 14 — their maximum number per individual), three preservation classes were identified, of which class 1 corresponds to poor, and class 3 — to good state of preser-vation. Comparison of sex and age groups per each preservation class using сhi-square test demonstrates that in the Gonur Depe skeletal remains of infants (0–4 years old) and young adults (under 35 years of age) show the best state of preservation. Skeletons of elderly adults (over 35 years of age) have the worst state of preservation. There are no statistically significant differences between sexes in the degree of bone preservation. On the one hand, these results, contrary to theoretical expectations, testify against the existence of taphonomic biases in preservation of infant and female skeletons. On the other hand, the underrepresentation of elderly individuals in the studied collection is probably explained by a decrease in resistance to taphonomic processes due to the ac-celerated loss of bone calcium. It has been concluded that age and sex-related biases in the demographic struc-ture of prehistoric skeletal samples cannot be universally explained by the preservation factor. Misrepresentation in the percentage of different age and sex groups is a non-linear and a complicated process that requires consid-eration of different factors affecting the qualitative and quantitative composition of a particular skeletal sample. A detailed assessment of the taphonomic characteristics of a studied skeletal collection should be a mandatory step prior to its analysis by paleodemographic methods.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".