Pollination ecology in the 21st Century: Key questions for future research
Bibliographic record
Abstract
To inspire new ideas in research on pollination ecology, we list the most important unanswered questions in the field. This list was drawn up by contacting 170 scientists from different areas of pollination ecology and asking them to contribute their opinion on the greatest knowledge gaps that need to be addressed. Almost 40% of them took part in our email poll and we received more than 650 questions and comments, which we classified into different categories representing various aspects of pollination research. The original questions were merged and synthesised, and a final vote and ranking led to the resultant list. The categories cover plant sexual reproduction, pollen and stigma biology, abiotic pollination, evolution of animal-mediated pollination, interactions of pollinators and floral antagonists, pollinator behaviour, taxonomy, plant-pollinator assemblages, geographical trends in diversity, drivers of pollinator loss, ecosystem services, management of pollination, and conservation issues such as the implementation of pollinator conservation. We focused on questions that were of a broad scope rather than case-specific; thus, addressing some questions may not be feasible within single research projects but constitute a general guide for future directions. With this compilation we hope to raise awareness of pollination-related topics not only among researchers but also among non-specialists including policy makers, funding agencies and the public at large. download Appendix
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.104 | 0.014 |
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".