Mulches Used in Highbush Blueberry and Entomopathogenic Nematodes Affect Mortality Rates of Third-Instar Popillia japonica
Bibliographic record
Abstract
Popillia japonica Newman (Japanese beetle) is an invasive, polyphagous pest in North America, as adults feed on plant foliage and larvae on roots. Management in crops relies on foliar and soil applications of insecticides, but entomopathogenic nematodes (EPN) are effective biocontrol agents. In highbush blueberry, mulches (composts, woodshavings, sawdust, bark) are used for weed control and fertility. Therefore, our objective was to determine the effects of Heterorhabditis bacteriophora and Steinernema scarabaei on third-instar P. japonica in substrates commonly used as mulches in blueberry. In containers in the laboratory, larval mortality was 90–100% with H. bacteriophora for all substrates, but rates with S. scarabaei were lower and variable among substrates. A mixture of municipal compost + woodchips/sawdust resulted in 60% larval mortality without adding EPN, but few nematodes were recovered, indicating other causes of death. In a field microplot experiment in October, larval mortality rates were 50% at most for all EPN and substrate type combinations, likely due to lower than optimal soil and substrate temperatures for EPN survival and infectivity. Overall, a compost and woodchip/sawdust mulch should help suppress P. japonica populations in blueberry, and applying H. bacteriophora when temperatures are optimal to mulches can provide excellent larval control.
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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) | 0.001 | 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) | 0.003 | 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".