Bibliographic record
Abstract
Nucleic acids, which hold clues to the evolution of various animal and hominid taxa, are comparatively weak molecules from other cellular debris, and thus evolutionary biologists are in essence time trapped. Fortunately, DNA and protein fragments do exist in fossil remains beyond what theoretical experimentation would suggest. Sequestering of DNA molecules in humic or Maillard-like complexes likely represents a rich source of DNA molecules from the past, which have yet to be tapped. These molecules were impossible to acquire due to the selective nature of the polymerase chain reaction. Recently, however, rapid parallel pyrosequencing techniques, such as those used in metagenomics-based research, which, in theory, allow for the identification of all short nucleotide sequences in a sample in a non-selective approach, have the potential to allow the identification of all nucleic acids in a sample, and thus represent the way forward for ancient DNA. In theory, this new technology will allow the completion of genomes of extinct animals, plants, and microbes. I will discuss the benefits and pitfalls of this metagenomics approach to ancient DNA, highlighting our recent efforts underway to sequence the wooly mammoth genome as well as other fossil remains.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".