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Record W2991334122 · doi:10.1289/isee.2011.00383

SURVEILLANCE OF AGRICULTURAL PESTICIDE USE AND POTENTIAL ENVIRONMENTAL EXPOSURE

2011· article· en· W2991334122 on OpenAlexaffabout
Nichole A. Garzia, Karla Poplawski, Anne‐Marie Nicol, Paul A. Demers

Bibliographic record

VenueISEE Conference Abstracts · 2011
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Residue Analysis and Safety
Canadian institutionsOccupational Cancer Research CentreUniversity of British Columbia
Fundersnot available
KeywordsChlorothalonilHectarePesticideAgriculturePopulationGeographyCensusEnvironmental scienceEnvironmental protectionAgricultural scienceEnvironmental healthBiologyEcologyMedicine

Abstract

fetched live from OpenAlex

Background and Aims: Limited information exists on pesticide exposure in Canada, and some of the most prevalently used pesticides are classified as “possible” carcinogens by the International Agency for Research on Cancer (IARC). The objective is to characterize environmental exposure to “possibly” carcinogenic IARC pesticides used for agriculture in Canada. Methods: Region-specific annual agriculture use (AAU) estimates for selected pesticides are derived by multiplying crop production areas (hectares) obtained from the Interpolated Census of Agriculture (2006) by crop-specific intensity use weights (grams/hectare per year) developed using national and provincial pesticide use information. Total AAU (tonnes) is derived for each pesticide as the sum of active ingredient used on all crop types within a sub-provincial region. Regional estimates are mapped using a Geographic Information System (GIS). Population estimates corresponding to sub-provincial AAU estimates are determined using Census of Population (2006) data. These estimates represent the number of persons at risk of environmental exposure to “possibly” carcinogenic pesticides, by exposure potential (i.e. AAU estimate). Results: AAU estimates were derived for chlorothalonil in Western provinces: British Columbia (BC), Alberta (AB), Saskatchewan (SK). Regions that use chlorothalonil were classified into four ‘usage groups’ based on AAU (tonnes) quartile distribution: Low (>0-21), Low-Medium (>21-82), Medium-High (>82-273), High (>273-1597). The percent of the total provincial population living in ‘High’ chlorothalonil use regions are approximately: 16% (n=595,353) for BC; 24% (n=790,014) for AB; and 62% (n=594,712) for SK. When mapped, the regions in which these populations reside are identified. Regions with no chlorothalonil use were classified as ‘no use’ (0 tonnes); corresponding populations were considered to not be at risk (62% of BC population; 54% of AB population; 5.6% of SK population). Conclusions: Surveillance of IARC “possible” carcinogenic pesticides provides knowledge on environmental exposure potential, in terms of geographic variation and extent (population counts) across Canada.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.611

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.032
GPT teacher head0.190
Teacher spread0.158 · 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 routes2
Has abstractyes

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