Studying Past Ecosystems and Human Behaviors Using Environmental and Ancient DNA
Bibliographic record
Abstract
Isolating and studying degraded DNA from preserved organismal remains and environmental samples allows new inferences about past ecosystem compositions, population dynamics, and, in the context of archaeological remains, human interactions with their environment. In this dissertation, I addressed how sequencing depth and stochasticity of metabarcoding PCR influences various measures of biodiversity. I found that sequencing depth and stochasticity between PCR replicates significantly influence estimates of alpha but not beta diversity. In my second chapter, I used eDNA isolated from permafrost cores spanning the last 50,000 years in the Klondike, Canada to characterize community composition and turnover of plant and mammalian communities. In this chapter, I characterized floral and faunal change over the last 50,000 years, with clear shifts from steppe to boreal forest habitat delineated with the presence and absence of arctic ground squirrels and woody plants. Finally, I isolated ancient DNA from archaeological moccasins to observe hunting patterns of Bison used by occupants of the Promontory Caves of Utah, an archaeological site occupied 1240-1290 AD. I found the majority (87%) of moccasins were constructed from female bison, supporting prior hypotheses of hunting strategies targeting cow-calf herds at the end of fall preparing for overwintering. My dissertation highlights some of the many questions that degraded DNA present in soil, bone, and preserved hides can contribute towards answering.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".