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Record W4211205190 · doi:10.53660/clm-100-120

Agrotóxicos em poços artesianos do Sudoeste paranaense e alterações genéticas em indivíduos abastecidos por esses poços

2022· article· pt· W4211205190 on OpenAlexaff
Diana Paula Perin, Alini de Almeida, Edinéia Paula Sartori Schmitz, Vitória Karolini Fieldkircher, Flavio Sokal, Denise Palma, Liziara da Costa Cabrera, Gisele Louro Peres, Dalila Moter Benvegnú

Bibliographic record

VenueConcilium · 2022
Typearticle
Languagept
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsMolecular biologyBiologyArt

Abstract

fetched live from OpenAlex

Os pesticidas são usados para controle de insetos e ervas daninhas, porém, podem contaminar diversos cursos d’água. Assim, esta pesquisa vem na perspectiva de avaliar a exposição a agrotóxicos da comunidade rural de um município localizado no sudoeste paranaense através da identificação de princípios ativos de defensivos agrícolas, da caracterização físico-química e microbiológica da água de poços artesianos, aliado a análise de frequência de micronúcleos e brotos nucleares em linfócitos dos indivíduos abastecidos por estes poços. Os resultados mostram a presença de diversas moléculas de agrotóxicos nas amostras analisadas, variando de duas a nove moléculas por poço. Os agrotóxicos mais encontrados foram o pirimicarb e imazetapir. Algumas das moléculas ultrapassaram o valor permitido pela legislação em águas para o consumo. As análises físico-químicas e microbiológicas encontram-se dentro dos valores estabelecidos para consumo. Duas das comunidades apresentam médias significativamente maiores de brotos em comparação ao grupo controle e o poço com maior número de agrotóxicos apresentou maior frequência de micronúcleos em relação ao grupo controle.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.273
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.001
Open science0.0020.006
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0530.005

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 head0.018
GPT teacher head0.235
Teacher spread0.217 · 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; both teacher heads agree on what is shown here.

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
Published2022
Admission routes1
Has abstractyes

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