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Blueberry IPM: Past Successes and Future Challenges

2019· review· en· W2909522853 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAnnual Review of Entomology · 2019
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicInsect-Plant Interactions and Control
Canadian institutionsAgriculture and Agri-Food Canada
FundersAgriculture and Agri-Food CanadaNational Institute of Food and AgricultureU.S. Department of Agriculture
KeywordsIntegrated pest managementBroad spectrumBiologyBiotechnologyPest controlCrop protectionProduction (economics)AgroforestryBusinessRisk analysis (engineering)EcologyEconomics

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.973
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.307
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it