Impact of plastic rain shields and exclusion netting on pest dynamics and implications for pesticide use in apples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Apple production is among the most pesticide-intensive cultures. Recently, plastic rain shields and pest exclusion netting have emerged as potential measures to reduce the heavy reliance on chemical pesticides in apple, due to their inhibitory effect on pathogen and pest infestations. In a field trial, we compared yields, pest, and pathogen abundance in an orchard consisting of four plots, where two plots were covered with anti-hail net covers, one with plastic rain shields only, and one with plastic rain shields and exclusion netting. Pests and pathogens were assessed visually, and beating tray samples were collected to compare overall arthropod diversity between plots. We observed virtually no scab infections in both plastic rain shield plots, despite a more than 70% reduction of fungicides applied, when compared to anti-hail plots. Although no codling moth insecticides were sprayed in the plot with exclusion netting we found significantly reduced damage here, when compared to the anti-hail plots. However, likely due to microclimatic changes, we observed an increase of powdery mildew, woolly apple aphids, and spider mites under plastic rain shields. Modeling of metabolic rates of arthropod herbivores and predators revealed that there is an increased potential of herbivory under plastic rain shields. However, in terms of plant protection, the net effect of plastic rain shields and exclusion netting was a substantial reduction in chemical pesticide use, demonstrating that they represent a promising approach to minimize the use of chemical pesticides in apple production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it