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Record W4293199784 · doi:10.1002/ps.7148

Transforming the evaluation of agrochemicals

2022· article· en· W4293199784 on OpenAlexaff
Douglas C. Wolf, Yadvinder Bhuller, Rhian B. Cope, Marco Corvaro, A Currie, John E. Doe, Adriana M. Doi, Gina M. Hilton, Jyotigna Mehta, David Saltmiras, Fiona Sewell, Maria Trainer, Sandrine E. Déglin

Bibliographic record

VenuePest Management Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsPublic Health Agency of CanadaHealth Canada
Fundersnot available
KeywordsAgrochemicalRisk analysis (engineering)AgricultureCognitive reframingComputer scienceBusiness

Abstract

fetched live from OpenAlex

The present agrochemical safety evaluation paradigm is long-standing and anchored in well-established testing and evaluation procedures. However, it does not meet the present-day challenges of rapidly growing populations, food insecurity, and pressures from climate change. To transform the current framework and apply modern evaluation strategies that better support sustainable agriculture, the Health and Environmental Sciences Institute (HESI) assembled a technical committee to reframe the safety evaluation of crop-protection products. The committee is composed of international experts from regulatory agencies, academia, industry and nongovernmental organizations. Their mission is to establish a framework that supports the development of fit-for-purpose agrochemical safety evaluation that is applicable to changing global, as well as local needs and regulatory decisions, and incorporates relevant evolving science. This will be accomplished through the integration of state-of-the-art scientific methods, technologies and data sources, to inform safety and risk decisions, and adapt them to evolving local and global needs. The project team will use a systems-thinking approach to develop the tools that will implement a problem formulation and exposure driven approach to create sustainable, safe and effective crop protection products, and reduce, replace and refine animal studies with fit-for-purpose assays. A new approach necessarily will integrate the most modern tools and latest advances in chemical testing methods to guarantee the robust human and environmental safety and risk assessment of agrochemicals. This article summarizes the challenges associated with the modernization of agrochemical safety evaluation, proposes a potential roadmap, and seeks input and engagement from the broader community to advance this effort. © 2022 Health and Environmental Sciences Institute (HESI). Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

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 imitation

Not 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.

metaresearch head score (Codex)0.093
metaresearch head score (Gemma)0.086
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.093
Threshold uncertainty score0.492

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0930.086
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.004
Science and technology studies0.0010.013
Scholarly communication0.0160.016
Open science0.0040.009
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.068
GPT teacher head0.317
Teacher spread0.249 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations19
Published2022
Admission routes1
Has abstractyes

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