Effects of land use type and seasonal climate on ground nesting wild bees
Bibliographic record
Abstract
Abstract Agricultural intensification leads to wide ranging changes in habitats along with reduction in nesting site availability and flower resources for wild pollinators. Yet, little is known about the impact of these changes on functional traits of communal ground‐nesting bees. This study assesses the abundance and body size of a common and widespread North American ground‐nesting bee, Agapostemon virescens , throughout three consecutive years at three land use types: (i) unmowed meadows, (ii) conventional pesticide use and mowed agricultural and (iii) organic pesticide‐free mowed landscapes. We found no difference in abundance among the three land use types, but body size of spring bees was smaller at farmlands than meadow sites. Body size also varied among years, and bees were smaller in years that followed warm and dry summer seasons. Spring bees were particularly small at organic farms in years following dry and warm summers. Our results suggest that the smaller size of overwintering bees at agricultural lands could compromise their long‐term survival. This study indicates that a higher frequency of dry and warm summers as a consequence of climate change can impede bee populations in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.002 | 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".