PalaeoChip Arctic1.0: An optimised eDNA targeted enrichment approach to reconstructing past environments
Bibliographic record
Abstract
Summary Ancient environmental DNA has been established as a viable biomolecular proxy for tracking taxonomic presence through time in a local environment, even in the total absence of primary tissues. It is thought that sedimentary ancient DNA (sedaDNA) survives through mineral binding. And while these organo-mineral complexes likely facilitate long-term preservation, they also challenge our ability to release and isolate target molecules. Two limitations in sedaDNA extraction impede many palaeoenvironmental reconstructions: the post-extraction carryover of enzymatic inhibitors, and sedaDNA loss when attempting to reduce inhibitor co-elution. Here, we present an optimised eDNA targeted enrichment approach for reconstructing past environments. Our new extraction protocol with targeted enrichment averages a 14.6-fold increase in on-target plant and animal DNA compared to a commercial soil extraction kit, and a 22.6-fold increase compared to a PCR metabarcoding approach. To illustrate the effectiveness of the PalaeoChip Arctic1.0 protocol, we present results of plant and animal presence from permafrost samples and discuss new potential evidence for the late survival (ca. 9685 BP) of mammoth ( Mammuthus sp .) and horse ( Equus sp .) in the Klondike Region of Yukon, Canada. This approach translates to a more diverse and sensitive dataset with increased sequencing efficiency of ecologically informative sedaDNA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".