Bee (Hymenoptera: Apoidea) diversity within apple orchards and old fields in the Annapolis Valley, Nova Scotia, Canada
Bibliographic record
Abstract
Abstract Bees are important within terrestrial ecosystems, providing pollination, which facilitates plant reproduction. Agricultural regions are large landscapes containing varying proportions of cropland, natural, and semi-natural habitats. Most bees are not restricted to any of these and move freely throughout, exploiting food and nesting resources in favourable locations. Many factors affect bee diversity, and knowledge of these is crucial for promoting healthy bee communities. The main objectives of this study were to compare diversity and guild structure of bee communities across a range of land disturbance levels within the Annapolis Valley, Nova Scotia, Canada, in habitats ranging from managed apple orchards to old fields. The two habitat extremes differed significantly; intensely managed orchards had significantly lower species richness (∼50%) than observed/estimated in old fields, but orchards with intermediate levels of adjacent natural/semi-natural habitat showed affinities to either extreme depending on the metrics used for estimating species richness. Species assemblages in orchards had lower proportions of several guilds, particularly cavity-nesters, bumble bees, and cleptoparasites, than other habitats. These guilds accounted for over 30% of bees collected in old fields but only 3–10% in orchards, increasing with habitat complexity. The use of guilds for assessing the health of bee communities is discussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".