Existing flower preference metrics disagree on best plants for pollinators: which metric to choose?
Bibliographic record
Abstract
Abstract When planting flowers for pollinator conservation, determining what flowers to plant is challenging because flower establishment can be time‐consuming and resource‐intensive. To alleviate this challenge, researchers have proposed methods to mathematically determine from plant–pollinator interaction data which flower species pollinators prefer, which can be defined as the likelihood that a flower species will be chosen by pollinators when offered on an equal basis with other flower species. We compared the flower lists produced by five sensible, peer‐reviewed preference metrics calculated from the same dataset and examined how each metric controls for flower abundance and relates to number of pollinator visits. We found little correlation between the ranked flower lists returned by each preference metric and that the metrics varied in the extent to which they controlled for abundance and provided different information than number of visits. The discordance among calculated flower preference lists is partially due to the different way each metric controls for abundance and suggests that these preference metrics need to be empirically tested and that more research is needed into the factors that impact pollinator floral preference. We discourage the use of three preference metrics (confidence interval, resource use and mass action hypothesis metrics), caution against the use of one (centrality metric) and recommend the use of the preference index metric due to its insensitivity to insufficient sampling, ease of use and the fact that it is not correlated with the number of pollinator visits.
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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 | 0.016 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".