Wild bee visitors and their association with sown and unsown floral resources in reconstructed pollinator habitats within an agriculture landscape
Bibliographic record
Abstract
Abstract In the Midwestern United States where the landscape has been largely converted from tallgrass prairie to row crops, thousands of private land parcels have been enrolled in federal pollinator habitat reconstruction programmes. To examine the outcome of pollinator habitat reconstruction, we randomly selected 19 sites that were in the third growing season post restoration and surveyed plant, floral and wild bee richness and abundance. Floral sources were divided into three categories: species from the seed mix (sown), species from the soil seed bank or surrounding landscape (unsown), and species that were likely unintentionally sown (contamination). Seventy‐two percent of captured bees were collected from sown flowers. The majority of oligolectic bees were Asteraceae specialists. Using linear regressions with bee abundance and richness as response variables and floral density and diversity as predictor variables, we showed that sown floral density was positively correlated with the total abundance and richness of bees collected from sown flowers, indicating that the selected pollinator friendly flowers provide support for wild bee communities. Conversely, total plant stem density or richness or the total floral density or richness was not significantly correlated with bee abundance or richness. The plant–pollinator network nestedness and modularity were positively correlated with the ratio of the unsown floral richness at each site, which suggests that the association between unsown flowers and wild bees can increase the long‐term stability of the plant‐pollinator network, even though the unsown species do not directly increase the bee abundance or richness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".