Using the Literature to Test Pollination Syndromes — Some Methodological Cautions
Bibliographic record
Abstract
“Pollination syndromes” are specific combinations of floral traits that are proposed to evolve convergently across angiosperm lineages in response to different types of animal pollinators. In spite of their long history, pollination syndromes have not been tested adequately–they rarely have been examined critically to determine how well they describe floral trait diversity or predict pollinators. In a recent meta-analysis of data from the literature, Rosas-Guerrero et al. (2014) provide a welcome test that draws on insights from past studies. At the same time, their study illustrates several difficulties of meta-analysis approaches in general, and for pollination biology in particular. Here we discuss those difficulties and propose some solutions. We first consider how to gather studies from the literature without introducing unintended bias, such as the old-fashioned method of working backward from cited literature. We next consider how to deal with difficulties that invariably arise when extracting and analyzing often-incomplete information from heterogeneous studies. Finally we discuss issues of interpreting and presenting the results in the most informative manner. We conclude that although Rosas-Guerrero et al. (2014) and other studies such as Ollerton et al. (2009) have arrived at different conclusions about the utility of pollination syndromes, their results are not necessarily incompatible.
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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 | 0.586 | 0.796 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.025 | 0.021 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.016 | 0.013 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".