Optimizing extraction and targeted capture of ancient environmental DNA for reconstructing past environments using the PalaeoChip Arctic-1.0 bait-set
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Sedimentary ancient DNA (sedaDNA) has been established as a viable biomolecular proxy for tracking taxon presence through time in a local environment, even in the total absence of surviving tissues. SedaDNA is thought to survive through mineral binding, facilitating long-term biomolecular preservation, but also challenging DNA isolation. Two common limitations in sedaDNA extraction are the carryover of other substances that inhibit enzymatic reactions, and the loss of authentic sedaDNA when attempting to reduce inhibitor co-elution. Here, we present a sedaDNA extraction procedure paired with targeted enrichment intended to maximize DNA recovery. Our procedure exhibits a 7.7–19.3x increase in on-target plant and animal sedaDNA compared to a commercial soil extraction kit, and a 1.2–59.9x increase compared to a metabarcoding approach. To illustrate the effectiveness of our cold spin extraction and PalaeoChip capture enrichment approach, we present results for the diachronic presence of plants and animals from Yukon permafrost samples dating to the Pleistocene-Holocene transition, and discuss new potential evidence for the late survival (~9700 years ago) of mammoth ( Mammuthus sp. ) and horse ( Equus sp. ) in the Klondike region of Yukon, Canada. This enrichment approach translates to a more taxonomically diverse dataset and improved on-target sequencing.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it