Data from: Avoiding dead ends: the experimental evolution of constraint as adaptation to environmental variation
Bibliographic record
Abstract
A bet-hedging strategy is suboptimal over short timescales, but optimal over long time scales because it buffers temporal variance in fitness. However, it is unclear how such strategies can persist when selection is expected to purge suboptimal traits in the short term. It has been proposed that the persistence of bet hedging is possible only if adaptive evolution is constrained in the short-term (Simons, 2002). To test the constraint-as-adaptation hypothesis, we take an experimental evolution approach using Saccharomyces cerevisiae and predict that evolution under reduced-frequency detrimental events results in an increase in evolution-resistant bet-hedging. Specifically, we evolve bet-hedging by imposing fluctuating selection through repeated heat shocks separated by intervening benign environments in which the frequency of extreme environments is reduced across two sequential evolution regimes (Regimes A and B). Then, to measure evolved constraints lines from both regimes are further evolved under extended benign conditions for ~150 generations and tested for the loss of heat shock tolerance. This dataset provides heat shock tolerance and competitive fitness data for replicate lines evolved in both regimes, and for the T1 ancestor. This dataset also provides these trait measurements after the replicate lines from both regimes are further evolved under extended benign conditions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 0.051 |
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".