Опыт определения копромаркеров в отложениях археологических памятников (по материалам стоянки Сельунгур, Южный Кыргызстан)
Bibliographic record
Abstract
Introduction. The article discusses the results of a study of ash layers from Holocene deposits at the Selungur (Surungur) Cave. Goals. So, the work attempts a practical evaluation of a method of identifying coprostanols in archaeological sediments. The investigation of Holocene deposits of the site was carried out in 2018 and 2021 field seasons. The study identifies a total of 7 Holocene layers that represent the typical ‘fumier’ facies of cave and rock shelter deposits connected to pastoralism and animal husbandry practices, and built up of stratified layers of burnt herbivore dung. The sequence contains a record of multiple earthquakes that disturbed the sediments and effected in water escape structures, plastic deformations, and faults. Nevertheless, the stratigraphy remains easily readable. Unfortunately, archaeological and paleofaunistic materials were never found but a series of ash-containing interlayers suggests that the cave was repeatedly visited by ancient humans. Materials. In the ash deposits, our micro charcoal analysis has identified areas of concentration of burnt dungs which was used for fire. Series of samples were taken throughout the section of Holocene deposits for gas mass spectrometry analysis. The research efforts have also included the analysis of modern dung from herbivores inhabiting the area, such as cows, sheep, goats, horses and donkeys, for the latter obtained data to serve as a reference collection. Results. Unfortunately, layer 7 of the Selungur Cave proved characterized by poor preservation of fire products, while layers 6–1 yielded somewhat rich data. The obtained results make it possible to identify coprosterols and determine that equine dung was used in layers 6-2 as a fuel, and goat dung — in the first layer. The most widespread distribution of equines in this region occurred during the existence of Dayuan (Parkan) state, in the third century BC and later in the Middle Ages. Most likely, during these periods the mountain corridor comprising the Selungur Cave could have been used as a pass of the Silk Road that connected the Fergana and Alay valleys. The accumulation of the upper layer, in our opinion, is associated with the modern era.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".