The Role of Templating in the Emergence of RNA from the Prebiotic Chemical Mixture
Bibliographic record
Abstract
Biological RNA is a uniform polymer in three senses: it uses nucleotides of a single chirality; it uses only ribose sugars and four nucleobases rather than a mixture of other sugars and bases; and it uses only 3'-5' bonds rather than a mixture of different bond types. We suppose that prebiotic chemistry would generate a diverse mixture of potential monomers, and that random polymerization would generate non-uniform strands of mixed chirality, monomer composition, and bond type. We ask what factors lead to the emergence of RNA from this mixture. We show that template-directed replication can lead to the emergence of all the uniform properties of RNA by the same mechanism. We study a computational model in which nucleotides react via polymerization, hydrolysis, and template-directed ligation. Uniform strands act as templates for ligation of shorter oligomers of the same type, whereas mixed strands do not act as templates. The three uniform properties emerge naturally when the ligation rate is high. If there is an exact symmetry, as with the chase of chirality, the uniform property arises via a symmetry-breaking phase transition. If there is no exact symmetry, as with monomer selection and backbone regioselectivity, the uniform property emerges gradually as the rate of template-directed ligation is increased.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".