Phenotypic selection on leaf ecophysiological traits in <i>Helianthus</i>
Bibliographic record
Abstract
Habitats that differ in soil resource availability are expected to differ for selection on resource-related plant traits. Here, we examined spatial and temporal variation in phenotypic selection on leaf ecophysiological traits for 10 Helianthus populations, including two species of hybrid origin, Helianthus anomalus and Helianthus deserticola, and artificial hybrids of their ancestral parents. Leaf traits assessed were leaf size, succulence, nitrogen (N) concentration and water-use efficiency (WUE). Biomass and leaf traits of artificial hybrids indicate that the actively moving dune habitat of H. anomalus was more growth limiting, with lower N availability but higher relative water availability than the stabilized dune habitat of H. deserticola. Habitats differed for direct selection on leaf N and WUE, but not size or succulence, for the artificial hybrids. However, within the H. anomalus habitat, direct selection on WUE also differed among populations. Across years, direct selection on leaf traits did not differ. Leaf N was the only trait for which direct selection differed between habitats but not within the H. anomalus habitat, suggesting that nutrient limitation is an important selective force driving adaptation of H. anomalus to the active dune habitat.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".