Resurrecting phenotypes from ancient DNA sequences: promises and perspectives
Bibliographic record
Abstract
Anatomical changes in extinct mammalian lineages over evolutionary time, such as the loss of fingers and teeth and the rapid increase in body size that accompanied the late Miocene dispersal of the progenitors of Steller’s sea cows (Hydrodamalis gigas (Zimmermann, 1780)) into North Pacific waters and the convergent development of a thick pelage and accompanying reductions in ear and tail surface area of woolly mammoths (Mammuthus primigenius (Blumenbach, 1799)) and woolly rhinoceros (Coelodonta antiquitatis (Blumenbach, 1799)), are prime examples of adaptive evolution underlying the exploitation of new habitats. It is likely, however, that biochemical specializations adopted during these evolutionary transitions were of similar or even greater biological importance. As these “living” processes do not fossilize, direct information regarding the physiological attributes of extinct species has largely remained beyond the range of scientific inquiry. However, the ability to retrieve genomic sequences from ancient DNA samples, combined with ectopic expression systems, now permit the evolutionary origins and structural and functional properties of authentic prehistoric proteins to be examined in great detail. Exponential technical advances in ancient DNA retrieval, enrichment, and sequencing will soon permit targeted generation of complete genomes from hundreds of extinct species across the last one million years that, in combination with emerging in vitro expression, genome engineering, and cell differentiation techniques, promises to herald an exciting new trajectory of evolutionary research at the interface of biochemistry, genomics, palaeontology, and cell biology.
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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 | 0.022 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.005 | 0.020 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".