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Record W1969945317 · doi:10.1520/jai103468

Development of Botanical Pesticides for Public Health

2011· article· en· W1969945317 on OpenAlexaff
Gretchen Paluch, Rod Bradbury, Steven Bessette

Bibliographic record

VenueJournal of ASTM International · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect Pest Control Strategies
Canadian institutionsAlpha Technologies (Canada)Systems, Applications & Products in Data Processing (Canada)
Fundersnot available
KeywordsPesticideEnvironmental sciencePublic healthMaterials scienceBiologyAgronomyMedicine

Abstract

fetched live from OpenAlex

Abstract Pesticide science is faced with a growing demand for green or sustainable pesticide chemistries that offer reduced risks to human health and the environment. Efforts are placed on the development of new pesticides containing novel active ingredients and/or formulations from natural sources. These products continue to drive innovation and have been proven to present effective alternatives to conventional pesticides. The use of botanical extracts for management of arthropods can be traced back through centuries, and their biological properties continue to be explored in the scientific literature. Many of the terpenoid compounds contained in plant essential oil extracts are capable of eliciting strong inhibitory effects against arthropods in laboratory settings; however, effective delivery can pose many challenges in the formulation process including selection of active ingredients, emulsifiers, spreaders, and other necessary components. Recent data demonstrates that select botanical terpenes/plant essential oils can control public health pests under field and laboratory settings, but current products may not necessarily encompass the full potential of their active ingredients. Further research continues to improve on botanical formulations and offer new approaches, such as microencapsulation, for use in product development. These findings highlight improvements in the formulation of plant essential oil active ingredients, and provide support for using botanicals to control public health pests.

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 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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.888
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.140
GPT teacher head0.284
Teacher spread0.144 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2011
Admission routes1
Has abstractyes

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