The bee community and its relationship to canola productivity in homogenous agricultural areas
Bibliographic record
Abstract
Canola crop productivity is benefited by bee pollination and it has been shown that bee communities can be affected by landscape composition. The aim of this study was to analyse the bee community and its relationship to canola seed production in agricultural areas. The density, abundance and richness of floral visitors of Brassica napus cultivar Hyola 61 in six commercial fields in southern Brazil were studied, and their relationships with seed production and the ratio of semi-natural, forested and agricultural areas surrounding the crops were examined. It was observed that canola fields of southern Brazil are surrounded by a homogeneous landscape dominated by agricultural areas. The survey of bees detected a low abundance and richness of native bees in contrast to the high abundance of Apis mellifera. Except for a negative correlation between the abundance of honey bees and the proportion of forested areas within a 2000 m radius from the field (R = -0.90; P = 0.012), no other correlations were found among bee abundance and richness and landscape composition. Although there was not a relationship between A. mellifera and seed set, there was a positive correlation between insect density and seed weight per plant (R = 0.87; P = 0.024). As honey bees were the most captured insect (79%), much of the pollination in this system was probably achieved by honey bees.
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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 | 0.001 | 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".