Flow cytometric analysis of pollen grains collected from individual bees provides information about pollen load composition and foraging behaviour
Bibliographic record
Abstract
BACKGROUND AND AIMS: Understanding the species composition of pollen on pollinators has applications in agriculture, conservation and evolutionary biology. Current identification methods, including morphological analysis, cannot always discriminate taxa at the species level. Recent advances in flow cytometry techniques for pollen grains allow rapid testing of large numbers of pollen grains for DNA content, potentially providing improved species resolution. METHODS: A test was made as to whether pollen loads from single bees (honey-bees and bumble-bees) could be classified into types based on DNA content, and whether good estimates of proportions of different types could be made. An examination was also made of how readily DNA content can be used to identify specific pollen species. KEY RESULTS: The method allowed DNA contents to be quickly found for between 250 and 9391 pollen grains (750-28 173 nuclei) from individual honey-bees and between 81 and 11 512 pollen grains (243-34 537 nuclei) for bumble-bees. It was possible to identify a minimum number of pollen species on each bee and to assign proportions of each pollen type (based on DNA content) present. CONCLUSIONS: The information provided by this technique is promising but is affected by the complexity of the pollination environment (i.e. number of flowering species present and extent of overlap in DNA content). Nevertheless, it provides a new tool for examining pollinator behaviour and between-species or cytotype pollen transfer, particularly when used in combination with other morphological, chemical or genetic techniques.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".