The importance of soil and vegetation characteristics for establishing ground-nesting bee aggregations
Bibliographic record
Abstract
Most bee species are ground-nesters, yet knowledge on the nesting behaviour of this diverse group remains sparse. Evidence on the effectiveness of ground-nesting bee species as crop pollinators is growing, but there is limited information on their nesting habits and preferences and how to manage habitats to enhance populations on farms. In this study, artificially prepared plots of bare soil were constructed with the aim to attract ground-nesting bees to nest in a commercial orchard in Kent, UK. Nine soil parameters were measured to determine their preferred soil properties: hydraulic conductivity, soil compaction, soil moisture, soil temperature, soil stoniness, soil organic matter, soil root biomass, soil texture and vegetation cover. Eighteen non-parasitic ground-nesting bee species (7 Andrena, 9 Lasioglossum, 1 Halictus and 1 Colletes spp.) were recorded in the study plots. Soil stoniness and soil temperature at 10cm depth were positively correlated, and vegetation cover and hydraulic conductivity were negatively correlated with the number of ground-nesting bees on the plots. We show that artificially created habitats can be exploited for nesting by several ground-nesting bee species. This study’s findings can inform management practices to enhance ground-nesting bee populations in agricultural and urban areas.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".