Unusual pollinator attractants increase the fructification rate on West Indian Cherry Trees
Bibliographic record
Abstract
Abstract Although West Indian Cherry (WIC) trees have abundant flowering, specimens of this species have low fructification rates, potentially associated with the dependence of these plants on pollinators for cross‐pollination and fruit production. We quantified fructification rates and assessed the market value of pollination services by conducting an experiment with six treatments, including manual and open pollination, pollinator exclusion treatment, and open pollination with blue and yellow attractants. The investigation occurred at two sampling periods (November–December 2015 and January–February 2016) in a commercial orchard of WIC in Brazil. Despite the six different treatments in the two sampling periods, the fructification rate only differed in open pollination treatments with colour attractants, increasing the fructification rate between 160% (for blue‐coloured attractants) and 240% (for yellow‐coloured attractants). Considering that yield is directly affected by the increase in the fructification rate, the yield might be enhanced by up to 70 ton/ha by the coloured attractants. Economically speaking, this result means an approximate 130% increase in the earnings for farmers and maybe transferable to other crops contributing to food production and recognizing the importance of biodiversity and associated ecosystem services.
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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".