The Emergence of RNA from the Heterogeneous Products of Prebiotic Nucleotide Synthesis
Bibliographic record
Abstract
Recent advances in prebiotic chemistry are beginning to outline plausible pathways for the synthesis of the canonical ribonucleotides and their assembly into oligoribonucleotides. However, these reaction pathways suggest that many noncanonical nucleotides are likely to have been generated alongside the standard ribonucleotides. Thus, the oligomerization of prebiotically synthesized nucleotides is likely to have led to a highly heterogeneous collection of oligonucleotides comprised of a wide range of types of nucleotides connected by a variety of backbone linkages. How then did relatively homogeneous RNA emerge from this primordial heterogeneity? Here we focus on nonenzymatic template-directed primer extension as a process that would have strongly enriched for homogeneous RNA over the course of multiple cycles of replication. We review the effects on copying the kinetics of nucleotides with altered nucleobase and sugar moieties, when they are present as activated monomers and when they are incorporated into primer and template oligonucleotides. We also discuss three variations in backbone connectivity, all of which are nonheritable and regenerate native RNA upon being copied. The kinetic superiority of RNA synthesis suggests that nonenzymatic copying served as a chemical selection mechanism that allowed relatively homogeneous RNA to emerge from a complex mixture of prebiotically synthesized nucleotides and oligonucleotides.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".