Climate limitations on the distribution and phenology of a large carpenter bee,<i>Xylocopa virginica</i>(Hymenoptera: Apidae)
Bibliographic record
Abstract
We studied climatic correlates of the geographic range of a common large carpenter bee ( Xylocopa virginica (L., 1771)), which reaches farther north than any other Xylocopa in North America. Computational models of the species’ range predicted that summer and winter temperatures limit its northern extent, whereas summer precipitation limits its western extent. We empirically evaluated the climatic constraints imposed by different seasons by examining the winter low-temperature tolerance of X. virginica, and the timing of activity during spring and summer. The bee’s absolute low-temperature tolerance (supercooling point) did not differ between two populations at mid- and high latitudes, and was in excess of requirements of a mean winter minimum temperature. Absolute minimum temperature tolerances may not directly influence the range of X. virginica, whereas other measures of cold tolerance, like exposure duration, might be more relevant. Between years within a study population, spring emergence dates of bees were significantly predicted by spring temperatures and weather (April: 6–11 °C; May: 13–17 °C). Between populations across the bee’s geographic range, bees in warmer climates were observed as much as 2–3 months earlier in the year. This suggests that a major constraint on the bee’s range is the length of the active season, which may be too short for brood development at high latitudes.
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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".