History repeats itself: genomic divergence in copepods
Bibliographic record
Abstract
Press stop, erase everything from now till some arbitrary time in the past and start recording life as it evolves once again. Would you see the same tape of life playing itself over and over, or would a different story unfold every time? The late Steven Jay Gould called this experiment replaying the tape of life and argued that any replay of the tape would lead evolution down a pathway radically different from the road actually taken (Gould 1989). This thought experiment has puzzled evolutionary biologists for a long time: how repeatable are evolutionary events? And if history does indeed repeat itself, what are the factors that may help us predict the path taken? A powerful means to address these questions at a small evolutionary scale is to study closely related populations that have evolved independently, under similar environmental conditions. This is precisely what Pereira et al. (2016) set out to do using marine copepods Tigriopus californicus, and present their results in this issue of Molecular Ecology. They show that evolution can be repeatable and even partly predictable, at least at the molecular level. As expected from theory, patterns of divergence were shaped by natural selection. At the same time, strong genetic drift due to small population sizes also constrained evolution down a similar evolutionary road, and probably contributed to repeatable patterns of genomic divergence.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".