Does Passive Sampling Accurately Reflect the Bee (Apoidea: Anthophila) Communities Pollinating Apple and Sour Cherry Orchards?
Bibliographic record
Abstract
During bloom of spring orchard crops, bees are the primary providers of pollination service. Monitoring these insects for research projects is often done by timed observations or by direct aerial netting, but there has been increasing interest in blue vane traps as an efficient passive approach to collecting bees. Over multiple spring seasons in Michigan and Pennsylvania, orchards were monitored for wild bees using timed netting from crop flowers and blue vane traps. This revealed a distinctly different community of wild bees captured using the two methods, suggesting that blue vane traps can complement but cannot replace direct aerial netting. The bee community in blue vane traps was generally composed of nonpollinating species, which can be of interest for broader biodiversity studies. In particular, blue vane traps caught Eucera atriventris (Smith), Eucera hamata (Bradley), Bombus fervidus (F.), and Agapostemon virescens (F.) that were never collected from the orchard crop flowers during the study period. Captures of bee species in nets was generally stable across the 3 yr, whereas we observed significant declines in the abundance of Lasioglossum pilosum (Smith) and Eucera spp. trapped using blue vane traps during the project, suggesting local overtrapping of reproductive individuals. We conclude that blue vane traps are a useful tool for expanding insights into bee communities within orchard crop systems, but they should be used with great caution to avoid local extirpation of these important insects.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".