Comment on “Does constructive neutral evolution play an important role in the origin of cellular complexity?” DOI 10.1002/bies.201100010
Bibliographic record
Abstract
Speijer has provided a critique of constructive neutral evolution and its role in the origin and evolution of cellular complexity. Not surprisingly, we disagree with his assertions. Because his description of the constructive neutral evolution model does not precisely conform to our view, as we and have elaborated it, we briefly re-state the model before addressing Speijer’s objections. The underlying premise of constructive neutral evolution is a pre-existing, essentially neutral interaction (RNA:RNA, RNA:protein, protein: protein) between component A, which has some activity, and component B. The activity of A is not dependent on the interaction with B, nor is A’s activity negatively influenced by this interaction. Thus, B could disappear from the scene without any effect on the ‘‘fitness’’ of A.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.042 | 0.031 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".