Importance of bee pollination for cotton production in conventional and organic farms in Brazil
Bibliographic record
Abstract
This study aimed to evaluate the importance of wild bee and feral honeybee visits for cotton production on conventional and organic farms. Experiments were conducted in Brazil, on a conventional cotton farm in Mato Grosso state in the Amazon biome and on an organic farm in Paraíba state in the Caatinga biome. On the conventional farm, bee assemblage and cotton production were measured near to and far from natural vegetation. Bee richness, fibre fraction, seed number and yield (Kg/ha) were higher by 57.14, 1.95, 17.77 and 18.44% respectively in plots near natural vegetation, but bee abundance did not vary with distance to natural vegetation. On the organic farm, because the cropping area is surrounded by natural vegetation, pollination deficit was evaluated using an exclusion experiment where cotton production of flowers bagged to prevent bee visitation (spontaneous self-pollination) was compared to production of flowers open to bee visitation (open pollination). Open pollinated flowers had higher average boll weight, fibre weight and seed number. Although cotton is not directly dependent on bee pollination, bees increased cotton production on the organic farm by more than 12% for fibre weight and over 17% for seed number. Our data confirm the importance of maintaining communities of pollinators on cotton farms, especially for organic production.
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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 | 0.000 | 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".