Data from: Pollen limitation and its influence on natural selection through seed set
Bibliographic record
Abstract
Stronger pollen limitation should increase competition among plants, leading to stronger selection on traits important for pollen receipt. The few explicit tests of this hypothesis, however, have provided conflicting support. Using the arithmetic relationship between these two quantities, we show that increased pollen limitation will automatically result in stronger selection (all else equal) although other factors can alter selection independently of pollen limitation. We then test the hypothesis using two approaches. First, we analyze published studies containing information on both pollen limitation and selection. Second, we explore how natural selection measured in one Ontario population of Lobelia cardinalis over three years and two Michigan populations in one year relates to pollen limitation. For the Ontario population we also explore whether pollinator-mediated selection is related to pollen limitation. Consistent with the hypothesis, we find an overall positive relationship between selection strength and pollen limitation both among species and within L. cardinalis. Unexpectedly, this relationship holds even for vegetative traits among species, and was not found in L. cardinalis for pollinator-mediated selection on nearly all trait types.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 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 | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.029 |
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".