Advances in cranberry insect pest management: A literature synthesis
Bibliographic record
Abstract
Over the past three decades, an increasing body of entomological research has been published on integrated pest management (IPM) in cranberries (Vaccinium macrocarpon Aiton). However, no paper has been published that synthesizes the existing literature. This paper fills this gap by analyzing 139 peer- and editor-reviewed articles that were data driven and had direct relevance to the subject of insect pests or insect pest management of V. macrocarpon. Results show that the top three studied insect pests of cranberries have been Sparganothis fruitworm (Sparganothis sulfureana Clemens), blackheaded fireworm (Rhopobota naevana Hübner), and cranberry fruitworm (Acrobasis vaccinii Riley). The regions with the most published entomological papers on cranberry IPM research have been New Jersey, Massachusetts, and Wisconsin in the United States, followed by British Columbia in Canada. Among IPM tactics, published research on chemical control, as well as on host-plant resistance, has increased likely due to recent advances on newer, reduced-risk insecticides and high-yielding cultivars; while published research focusing on behavioral control has declined likely due to the cost of these tactics. There are no consistent trends in published research on natural and biological control or cultural control. These historical research trends are important when considering regulatory changes on insecticide use, such as the Food Quality Protection Act of 1996 in the United States, which resulted in the banning and restrictions of certain broad-spectrum insecticides. As more insecticides are banned or restricted and global and organic cranberry production increases, we anticipate further advances in research related to sustainable IPM tactics.
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.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".