Assessing floral trait variation in Platanthera dilatata (Orchidaceae) across an elevational gradient
Bibliographic record
Abstract
Flower morphology often changes over altitude, although the patterns themselves can be variable, with flowers being either smaller or larger. Floral trait variation is often considered in the context of pollinator-mediated selection. However, other explanations, including underlying genetics and plasticity, resource availability and floral enemies have been proposed. Here, we assess 10 floral traits in Platanthera dilatata var. dilatata across an elevational gradient on Vancouver Island, British Columbia, Canada, to determine if floral traits vary with altitude. We find that floral traits are larger at the lowest elevation site. However, much of the floral trait variation appears to be driven by temperature, which is not necessarily correlated with the altitudinal gradient. Given the intrinsic link between climate and resource availability, we suggest that resource availability confers a local selection pressure on floral trait size that may be balanced at larger spatial scales by antagonistic pressure from shared pollinators. Direct investigations of the environmental and genetic factors driving floral trait variation are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".