How a neutral evolutionary ratchet can build cellular complexity
Bibliographic record
Abstract
Complex cellular machines and processes are commonly believed to be products of selection, and it is typically understood to be the job of evolutionary biologists to show how selective advantage can account for each step in their origin and subsequent growth in complexity. Here, we describe how complex machines might instead evolve in the absence of positive selection through a process of "presuppression," first termed constructive neutral evolution (CNE) more than a decade ago. If an autonomously functioning cellular component acquires mutations that make it dependent for function on another, pre-existing component or process, and if there are multiple ways in which such dependence may arise, then dependence inevitably will arise and reversal to independence is unlikely. Thus, CNE is a unidirectional evolutionary ratchet leading to complexity, if complexity is equated with the number of components or steps necessary to carry out a cellular process. CNE can explain "functions" that seem to make little sense in terms of cellular economy, like RNA editing or splicing, but it may also contribute to the complexity of machines with clear benefit to the cell, like the ribosome, and to organismal complexity overall. We suggest that CNE-based evolutionary scenarios are in these and other cases less forced than the selectionist or adaptationist narratives that are generally told.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".