Pollen Removal and Deposition by Pollen- and Nectar-Collecting Specialist and Generalist Bee Visitors to <i>Iliamna bakeri</i> (Malvaceae)
Bibliographic record
Abstract
Up to 60% of the bee species of a region are oligolectic; they collect pollen only from a closely related group of plants though nectar-collecting choices are often broader. Bee specialists are expected to be superior to generalists in gathering pollen from their host plants and perhaps in transferring pollen to host stigmas. We used the oligolege Diadasia nitidifrons and its pollen-host Iliamna bakeri to ask if specialists 1) were more efficient than generalists as pollen-collectors; 2) deposited more pollen on stigmas than generalists; and 3) if pollen-collectors removed and deposited more pollen than did nectar-collectors. We found support for the first and third hypotheses. Diadasia pollen- and nectar-collectors removed more pollen per flower-visit than did their primary generalist competitors (Agapostemon spp.). The superior pollen-gathering efficiency of Diadasia exceeded differences that might be attributed to size: although Agapostemon females are, on average, 12.5% smaller than Diadasia females, pollen-collecting Agapostemon left 22.9% more pollen in flowers than did Diadasia. We found no difference between taxa in time spent foraging on a single flower. Diadasia and Agapostemon pollen-collectors deposited significantly more pollen on I. bakeri stigmas than did nectar-collectors; there was no difference between taxa in pollen deposition. Diadasia was superior to generalists as a pollinator in two ways: Diadasia was 1) a more reliable presence in I. bakeri populations; and 2) always most abundant at I. bakeri flowers. The association between D. nitidifrons and I. bakeri appears to be another example of a highly specialised bee affiliated with an unspecialised host-plant.
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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 | 0.000 | 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".