Data from: Water availability as an agent of selection in introduced populations of Arabidopsis thaliana: impacts on flowering time evolution
Bibliographic record
Abstract
Flowering is one of the most influential events in the life history of a plant and one of the main determinants of reproductive investment and lifetime fitness. It is also a highly complex trait controlled by dozens of genes. Understanding the selective pressures influencing time to flowering, and being able to reliably predict how it will evolve in novel environments, are unsolved challenges for plant evolutionary geneticists. Using the model plant species, Arabidopsis thaliana, we examined the impact of simulated high and low winter precipitation levels on the flowering time of naturalized lines from across the eastern portion of the introduced North American range, and the fitness consequences of early versus late flowering. Flowering time order was significantly correlated across two environments—in a previous common garden experiment and in environmental chambers set to mimic mid-range photoperiod and temperature conditions. Plants in low water flowered earlier, had fewer basal branches and produced fewer fruits. Selection in both treatments favored earlier flowering and more basal branches. Our analyses revealed an interaction between flowering time and water treatment for fitness, where flowering later was more deleterious for fitness in the low water treatment. Our results are consistent with the hypothesis that differences in winter precipitation levels are one of the selective agents underlying a flowering time cline in introduced A. thaliana populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.021 |
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".