Generalist versus specialist pollination systems in 26 <i>Oenothera</i> (Onagraceae)
Bibliographic record
Abstract
Although generalized and specialized plants are often discussed as alternative states, the biological reality may better be viewed as a continuum. However, estimations of pollinator specificity have been confounded in some studies by the assumption that all floral visitors are pollinators. Failure to account for pollen load can lead to inaccurate conclusions regarding the number of pollinators with which a species actually interacts. The aim of this study was to clarify the distribution of pollination-system specialization within one clade, using a more rigorous assessment of pollen flow. The genus Oenothera has long been used as a model system for studying reproductive biology, and it provides a diversity of pollination systems and a wealth of historical data. Both floral visitation rate and pollen-load analysis of sampled pollinators, combined into a metric of pollen flow, were used to quantify the pollination systems of 26 Oenothera taxa. Metric of pollinator specialization were calculated as functions of both total pollinator taxa, and as pollinator functional groups. We found that for Oenothera, the number of floral visitors highly overestimates the number of pollinators, and is inadequate for determining or predicting pollination system specialization. We found that that pollination systems were distributed on a gradient from generalized to specialized, with more pollinator-specialized plant taxa, especially when estimated using pollinator functional groups. These results are in conflict with previous studies that depict most plant species as generalists, and this finding may be related to how prior studies have estimated specialization.
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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".