Effect of Cultural Practices on Neopamera bilobata in Relation to Fruit Injury and Marketable Yields in Organic Strawberries
Bibliographic record
Abstract
The strawberry seed bug, Neopamera bilobata (Say), is an emerging pest of organic and conventional strawberries in Florida. There is limited information on this Rhyparochromidae species. Thus, the type of injury caused is not clearly documented and management recommendations are lacking. In this study, we evaluated the effect of strawberry cultivars, cover crops, and the presence of runners on N. bilobata populations and yield. We also investigated the effect of select cultivars and the presence of runners on N. bilobata injury levels. In addition, we used fruit bagging experiments to investigate the effects of N. bilobata population and life stage (nymph vs. adult) on strawberry fruits. There was no effect of cover crop or cultivar on N. bilobata populations. In the 2017–2018 season, strawberry plots with runners contained higher N. bilobata populations compared with plots without runners, and adult infestation was significantly higher than nymphal infestation. In the 2018–2019 season, the trend was reversed with higher numbers of N. bilobata collected in plots with runners removed. In the 2019–2020 season, there was no significant difference in N. bilobata populations in plots with and without runners. In both 2018–2019 and 2019–2020, nymphal infestation was higher than adult infestation. Less injury was recorded in “Florida Brilliance” compared with the other cultivars tested. In the 2019–2020 season, less injury was recorded from plots without runners while the difference was not significant in 2017–2018 or 2018–2019. Releasing five and ten adult N. bilobata on ripe (red) fruit produced a similar level of injury while no injury to unripe (green) fruit was observed. Both adults and nymphs cause injury to ripe fruit. These findings can help contribute to the development of an integrated pest management program for strawberry N. bilobata.
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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 | 0.001 | 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".