Natural Selection, Variation, Adaptation, and Evolution: A Primer of Interrelated Concepts
Bibliographic record
Abstract
Natural selection is an elegantly simple concept but one that can manifest in complex ways. I review how the basic model of single-trait viability selection has been extended to more complex forms of selection on multiple traits and on reaction norms. Fitness is defined as the expected lifetime reproductive success for individuals with a given genotype or phenotype over a given range of environments. Since the reproductive success realized by any individual will include a stochastic departure from this expectation, selection is therefore a consistent difference in fitness between organisms with different characteristics. A clear distinction is drawn between selection, which can act on any phenotypic difference, and the response to selection, which can occur only if phenotypic differences are heritable. This distinction separates the action of natural selection in filtering variation from the origin of the novel variants on which selection acts. Since selection frequently acts on standing genetic variation or on conditionally neutral variation, both of which accumulate in populations before the imposition of selection, such variation accumulates independently of its fitness effects under the subsequent selection regime. Recent discussions of “Lamarckian” inheritance must be carefully circumscribed to avoid the implication of directed mutation, for which there is no evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.026 |
| Scholarly communication | 0.007 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".