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Record W2329387591 · doi:10.1021/jf5060744

Developing Global Leaders for Research, Regulation, and Stewardship of Crop Protection Chemistry in the 21st Century

2015· article· en· W2329387591 on OpenAlexaff
John Unsworth, Camilla Corsi, Jeanette M. Van Emon, Annemieke Farenhorst, Denis Hamilton, Cody J. Howard, Robert Hunter, Jeffrey J. Jenkins, G.A. Kleter, Rai S. Kookana, Joseph O. Lalah, Michael Leggett, Karina S.B. Miglioranza, Hisashi Miyagawa, Natalia Peranginangin, Baruch Rubin, Bipul Saha, Najam A. Shakil

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGenetically Modified Organisms Research
Canadian institutionsUniversity of Manitoba
FundersInternational Union of Pure and Applied ChemistryDivision of ChemistryAmerican Chemical Society
KeywordsStewardship (theology)Crop protectionChemistryPolitical scienceEnvironmental ethicsBusinessBiotechnologyEnvironmental scienceAgroforestryBiologyLaw

Abstract

fetched live from OpenAlex

To provide sufficient food and fiber to the increasing global population, the technologies associated with crop protection are growing ever more sophisticated but, at the same time, societal expectations for the safe use of crop protection chemistry tools are also increasing. The goal of this perspective is to highlight the key issues that face future leaders in crop protection, based on presentations made during a symposium titled "Developing Global Leaders for Research, Regulation and Stewardship of Crop Protection Chemistry in the 21st Century", held in conjunction with the IUPAC 13th International Congress of Pesticide Chemistry in San Francisco, CA, USA, during August 2014. The presentations highlighted the fact that leaders in crop protection must have a good basic scientific training and understand new and evolving technologies, are aware of the needs of both developed and developing countries, and have good communication skills. Concern is expressed over the apparent lack of resources to meet these needs, and ideas are put forward to remedy these deficiencies.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.120

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 head0.135
GPT teacher head0.307
Teacher spread0.172 · 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 designBench or experimental
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

Citations21
Published2015
Admission routes1
Has abstractyes

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