A little-known world - assessing a non-bee crop flower visiting community using metabarcoding
Bibliographic record
Abstract
Pollinator diversity is critical for optimal ecosystem service and function. While bees are frequently the most efficient pollinators, they represent only a small fraction of pollinator diversity. Non-bee pollinators have received little recognition for their role in commercial agricultural pollination despite representing 95% of flower visitor diversity. Many non-bee pollinators are more resilient to land-use intensification and climate change due to their nomadic life-history and tolerance of inclement weather. Our research characterizes non-bee pollinator communities, their foraging preferences, and floral fidelity in strawberry crops. We caught 608 non-bee flower visitors, across three field sites, during three months of the flowering period (May–August) of day-neutral strawberries in southern Ontario. DNA metabarcoding provided species-level identifications of the non-bee flower visiting community. Diptera (64%) and Hymenoptera (22%) (primarily bee species) were the most abundant flower visitors; Coleoptera and Hemiptera were also collected from flowers. Metabarcoding of pollen identified pollen from 110 genera representing 48 different families. Species with a high floral fidelity (flower constancy) for visiting strawberries were likely to be more effective pollinators (vectors of conspecific pollen between reproductively receptive strawberry plants). Additionally, small amounts of pollen from other plant genera suggested that insects are active and mobile, rather than staying stationary on a single flower.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".