Forest remnants enhance wild pollinator visits to cashew flowers and mitigate pollination deficit in NE Brazil
Bibliographic record
Abstract
Pollination deficit could cause low yields in cashew (Anacardium occidentale) and it is possible that deforestation surrounding cashew plantations may prevent effective pollinators from visiting cashew flowers and contribute to this deficit. In the present work, we investigated the proximity effect of small and large forest fragments on the abundance and flower visits by feral Apis mellifera and wild native pollinators to cashew flowers and their interactions with yield in cashew plantations. Cashew nut yield was highest when plantations bordered a small forest fragment and were close to the large forest fragment. Yield from plantations that did not border small forest fragments but were close to the large forest fragment did not differ to yield from plantations at a greater distance to the large forest fragment. Flower visits by wild native pollinators, mainly Trigona spinipes, were negatively affected by distance to the large forest remnant and their numbers were directly correlated to nut yield. The number of A. mellifera visiting cashew flowers did not change significantly with distance to forest fragments, nor was it correlated with yield. We conclude that increasing the number of wild pollinator visits may increase yield, and proximity to large forest fragments are important for this.
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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".