Beyond beanbag genetics: Wright's adaptive landscape, gene interaction networks, and the evolution of new genetic systems
Bibliographic record
Abstract
Introduction Diversity and evolutionary innovation are the hallmarks of life. Life appears to defy the laws of physics and chemistry and it does so with the help of energy. Life succeeds by finding ways to overcome natural and physical hindrances. Life spread over the face of the globe and filled almost every crevice and habitat. Of course there are limits to what evolution can do. Everything is not possible. For example, there must be a limit to how tall California redwoods can grow just as there must have been a limit to the size of dinosaurs. Although life's success appears almost boundless and has had continued success for more than four billion years, there are no general laws describing the limits of evolution. This is in contrast to physical and chemical laws that define constraining limits. The grandest experiment of nature, the evolution of life, has no theory equivalent to E = mc 2 . The basic forces of evolution, both intrinsic (mutation, migration, sexuality, selection, and random genetic drift) and extrinsic (historical contingency and major catastrophe), generate not a single outcome but a range of possibilities.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".