Bee Community in Open-Field Tomato Crop and Pollination Effect by Wild Bees on the Fruit Production
Bibliographic record
Abstract
Bees are important components of the pollinator community of most ecosystems, contributing to the production of crops. The knowledge on the bees’fauna associated with crops and the pollination effect performed by bees on the fruit production and quality is important to the design, planning, and execution of projects to conserve pollinator populations in agricultural systems. The objectives of this work were to identify bees visiting tomato flowers, verify the climate variable and the day period on bee forage activity, and to evaluate the fruit production with different pollination types. The study was conducted from July 2015 to September 2017 in the Norte de Minas and Zona da Mata region, Minas Gerais state, Brazil. Eleven tomato fields were sampled. Fruit set and fruit quality from different pollination methods was evaluated with the following treatments: single visit (SV), open pollination (OP), mechanical pollination (MP) and control (self-pollination) (C). A total of 1,998 individuals distributed in Andrenidae, Apidae, and Halictidae families were collected, with greater abundance and species richness for Apidae. The tomato fruit mass was higher in the OP than in the SV and MP, which differed from the C. The high abundance of Exomalopsis analis and its occurrence in all fields, allied to its sonication behavior, indicate that this species is an important pollinator of the tomato. The tomato does not depend exclusively on bee pollination, but this improves its yield and quality, especially when performed by individuals of different species.
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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".