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Record W2780264326 · doi:10.5539/jas.v9n13p122

Pesticide Exposures Induce Male-Mediated Reproductive Toxicity: A Review

2017· review· en· W2780264326 on OpenAlexvenueno aff
Nur Afizah Yusoff, Siti Balkis Budin, Izatus Shima Taib

Bibliographic record

VenueJournal of Agricultural Science · 2017
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPesticide Exposure and Toxicity
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsBiologyPesticideInfertilityFertilitySemen qualitySpermReproductive toxicityPhysiologyToxicityToxicologyPregnancyMedicineEnvironmental healthEcologyGeneticsPopulationInternal medicine

Abstract

fetched live from OpenAlex

Infertility remains a continuing globally problem wherever couples worldwide were infertile as much as 42 million in 1990 and keep projecting to 48.5 million in 2010. Male-mediated infertility becomes one of the numerous concerns due to pesticide especially sperm quality as revealed with raising number of animal and human studies in latter-day among researchers. Pesticides have been used since the early days as pest control in agriculture, as vector controls in malaria and dengue as well as subject to much regulation. The reproductive system might be affected with some negative effects from pesticides that lead to interference with the male hormonal function. Polyunsaturated fatty acid (PUFA) content in the sperm cell membrane makes it become highly susceptible towards reactive oxygen species (ROS), thus leading to sperm damage. Besides known genetic and environmental factors, research during the last two decades has highlight on several mechanisms and their association with male infertility. The male germ line undergoes extensive epigenetic modifications throughout fetal to adult life hence vulnerable to environmental factors that may influence fertility. The present literature will help in understanding the mechanisms of pesticide in inducing male-mediated reproductive toxicity.

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)0.003
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.755

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0030.000
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 head0.090
GPT teacher head0.330
Teacher spread0.240 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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