Influence of Honey Bee (Hymenoptera: Apidae) Density on the Production of Canola (Crucifera: Brassicacae)
Bibliographic record
Abstract
Pollination is an essential step in the seed production of canola, Brassica napus L. It is achieved with the assistance of various pollen vectors, but particularly by the honey bee, Apis mellifera L. Although the importance of pollination has been shown for the production of seed crops, the need to introduce bee hives in canola fields during flowering to increase oil seed yield has not yet been proven. With the purpose of showing this, hives of A. mellifera were grouped and placed in various canola fields in the Chaudière-Appalaches and Capitale-Nationale regions (nine fields; three blocks with three treatments; 0, 1.5, and 3 hives per hectare). A cage was used to exclude pollinators and bee visitations were observed in each field. After the harvest, yield analyses were done in relation to the bee density gradient created, by using pod set, number of seeds per plant, and weight of 1000 seeds. Results showed an improvement in seed yield of 46% in the presence of three honey bee hives per hectare, compared with the absence of hives. The introduction of honey bees contributed to production and consequently, these pollinators represented a beneficial and important pollen vector for the optimal yield of canola.
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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".