Variation in flower biomass among nearby populations of <i>Impatiens textori</i> (Balsaminaceae): effects of population plant densities
Bibliographic record
Abstract
Although most of the previously detected variation in flower biomass was among populations that were far apart from each other, differences in population characteristics (population size, area, and plant density) may bring the variation in flower biomass to a more local scale. To examine the variation in flower biomass among nearby populations of Impatiens textori Miq. (Balsaminaceae), field studies were conducted on six natural populations located along a stream in Japan. We also examined the dependence of flower biomass and outcrossing rate on population characteristics, as well as the differences in plant size and pollinator behavior among populations. We conducted pollination experiments with potted plants, in which plant density and flower size were independently manipulated. Mean flower mass varied among populations, being negatively dependent on plant density. One-factor ANCOVA showed that both plant size and the other population-level factor affected flower biomass variation. Experiments with potted plant arrays showed that geitonogamous pollination more likely occurred in sparse populations, but in field studies, the population outcrossing rate was not significantly dependent on plant density of the population. Thus, the variation in flower biomass cannot be fully explained by these commonly considered factors. Our results show that the flower biomass of populations may evolve locally in response to plant density or other population characteristics.Key words: variation, flower biomass, population, Impatiens textori, plant density, local scale.
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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 | 0.001 | 0.001 |
| 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".