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Record W2033675455 · doi:10.1080/10937400600755895

Addressing the Linkage between Exposure to Pesticides and Human Health Effects—Research Trends and Priorities for Research

2006· review· en· W2033675455 on OpenAlexafffundabout
Leonard Ritter, N.C.I. Goushleff, Tye E. Arbuckle, Donald C. Cole, Mark Raizenne

Bibliographic record

VenueJournal of Toxicology and Environmental Health Part B · 2006
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsUniversity of TorontoHealth CanadaUniversity of Guelph
FundersCanadian Institutes of Health ResearchHealth Canada
KeywordsHuman healthPesticidePublic healthEnvironmental healthPolitical scienceBusinessEnvironmental planningMedicineGeographyBiology

Abstract

fetched live from OpenAlex

In recent years, there has been escalating concern over the possible association between exposure to pesticides and adverse human health effects by a number of non-governmental organizations, professional and public interest groups. Recognizing the need to document the scientific basis of these concerns as a foundation for initiating a research theme devoted to linkages between exposures to pesticides and human health effects, the Canadian Institutes of Health Research (CIHR) requested a summary of recent research trends that address these linkages. Experts across Canada in the field of pesticide regulation and research were invited to participate in the review. The review summarizes the limitations of past and current studies related to pesticides and human health effects research and makes suggestions for future research priorities and proposed study designs that will improve the assessment of pesticide exposure, the associated health risks, and improved methodology for regulatory decision making.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.352
GPT teacher head0.484
Teacher spread0.132 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations43
Published2006
Admission routes3
Has abstractyes

Explore more

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