Palaeoecological Perspectives on the Peopling of the Americas: Sedimentary Ancient DNA Analysis of Early Archaeological Sites
Bibliographic record
Abstract
Detailed and accurate paleoenvironmental reconstruction is crucial to interpreting past human activity. Sedimentary ancient DNA (sedaDNA) analysis augments traditional methods of paleoenvironmental reconstruction by identifying taxa for which there are no visible fossil remains revealing novel paleoenvironmental insights. However, the application of sedaDNA analysis to archaeological contexts remains limited as standardized sampling procedures and strategies have yet to be developed. The central research aim of this dissertation is to assess the feasibility of recovering sufficient sedaDNA from archaeological sites in novel depositional settings to reconstruct paleoenvironments. The first paper addresses the limited application of sedaDNA analysis to archaeological sites by providing an accessible overview of sedaDNA methodologies and developing optimized sampling strategies to facilitate future implementation of sedaDNA analysis in archaeological contexts. The second and third papers expand the current geographic applications of sedaDNA analysis by demonstrating that ancient DNA can be recovered from sediments in novel depositional settings (e.g., open air sites). The second paper demonstrates feasibility of extracting sedaDNA from non-frozen loess deposits at Bluefish Cave III in the Yukon Territory, Canada to reconstruct the paleoenvironment of eastern Beringia through the Last Glacial Maximum. SedaDNA from Bluefish Cave III reveals distinct shifts in floral and faunal community composition confirming hypotheses that eastern Beringia is a mosaic of steppe and mesic tundra environments. The third paper aims to reconstruct the paleoenvironments of two Paleoindian archaeological sites in Kansas (Claussen and Kanorado). Despite differential success in recovering sufficient sedaDNA to reconstruct paleoenvironments, these results suggest that informative sedaDNA can be recovered from open air mid-latitude sites significantly expanding the current geographic range of sedaDNA applications.
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.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".