Bibliographic record
Abstract
Abstract Attempts to predict macroevolution from microevolution, and microevolution from macroevolution, when natural selection is the main cause have met with varying success. Explanations for failure are numerous, but the reasons are uncertain even when a link is found. Here, I discuss possible explanations for outcomes of three efforts and ways to test them. First, quantitative genetic variation within populations often predicts directions of species divergence with surprising accuracy. Natural selection probably contributes to this pattern, but the evidence suggests that even long-term phenotypic evolution is influenced by enduring genetic biases. Second, the rate of evolution of reproductive isolation repeatedly fails to predict species diversification rates for unknown reasons. Suspicion falls on the influence of ecological and population demographic processes that might play a dominant role in the net rate of accumulation of species, an idea as yet little tested. Third, macroevolutionary patterns in the distribution of phenotypes of species in clades can in principle predict selection coefficients in diverging populations. I use the concept of adaptive landscape to suggest why the macroevolutionary signal of divergent selection is strongest at the time of splitting and why little information about selection coefficients from phylogenetic methods remains in the long run. Estimating adaptive landscapes from first principles would facilitate further efforts to link microevolution and macroevolution.
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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 | 0.022 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".