Cucurbits as a model system for crop pollination management
Bibliographic record
Abstract
Cucurbit crops have steadily increased in production over the last 50 years, particularly in Asia where pioneering technological advancements and genetic improvements have created new hybrid varieties. Generally, cucurbits are dependent on insect-pollination for fruit set and are popular species for pollination studies. This review systematically summarises pollination research conducted in the major food genera of cucurbits: Cucurbita, Cucumis, and Citrullus, to ask: 1) what are cucurbits’ requirement for pollination and their most effective pollinators? And 2) Does pollinator management increase pollinator visitation to, and yield of, cucurbit crops? These accounts of cucurbit pollination demonstrate that wild bee species such as Bombus terrestris, B. impatiens and Eucera spp. were frequently able to fulfil the pollination requirements of multiple cucurbit species. However, pollinator behaviour, pollen deposition on stigmas, and pollinators’ contribution to yield vary between cucurbit species and study site. Nonetheless, the provision of additional floral resources at both field and farm scales may help to encourage pollination of cucurbit species whilst supporting pollinators’ nutritional requirements beyond those already provided by the cucurbit crop. Synthesising studies on cucurbits’ requirement for pollination and how pollinators vary spatially and temporally in the landscape can extend beyond cucurbit systems to inform growers and pollination ecologists of other pollinator-dependent crop species wishing to maximise pollination services, species conservation; or both.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".