Blueberry IPM: Past Successes and Future Challenges
Bibliographic record
Abstract
Blueberry is a crop native to North America with expanding production and consumption worldwide. In the historical regions of production, integrated pest management (IPM) programs have been developed and provided effective control of key insect pests. These have integrated monitoring programs with physical, cultural, biological, behavioral, and chemical controls to meet the intense demands of consumers and modern food systems. Globalization of the blueberry industry has resulted in new pest-crop associations and the introduction of invasive pests into existing and new blueberry-growing areas. Invasive pests-in particular spotted wing drosophila-have been highly disruptive to traditional IPM programs, resulting in increased use of insecticides and the potential to disrupt beneficial insects. Moreover, regulatory agencies have reduced the number of broad-spectrum insecticides available to growers while facilitating registration and adoption of reduced-risk insecticides that have a narrower spectrum of activity. Despite these new tools, increasing international trade has constrained insecticide use because of maximum residue limits, which are often not standardized across countries. Great potential remains for biological, behavioral, cultural, and physical methods to contribute to blueberry IPM, and with more regions investing in blueberry research, we expect regionally relevant IPM programs to develop in the new production regions.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".