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Record W2608755274

Concentration and Off-target Movement of Neonicotinoid Residues During Agricultural Practices in Southwestern Ontario

2017· dissertation· en· W2608755274 on OpenAlexaboutno aff
Luis Gabriel Forero

Bibliographic record

VenueThe Atrium (University of Guelph) · 2017
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicInsect and Pesticide Research
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureChristian ministryAgricultural economicsGeographyNeonicotinoidAgricultural scienceEnvironmental protectionEnvironmental scienceAgronomyPolitical scienceArchaeologyPesticideEconomics
DOInot available

Abstract

fetched live from OpenAlex

Neonicotinoid insecticides are important globally to control pests in numerous agricultural crops, representing 24% of total agricultural insecticide use. Recently, exposure of non-target organisms to neonicotinoids present in various agricultural matrices is debated as a result of their use as seed treatments in field crops. Field sampling and laboratory studies were conducted from 2013 to 2016 to determine concentration and movement of soil bound residues and particulate matter exhausted from vacuum type planters during the planting of neonicotinoid treated seed. “Wick effect” was reported as an important process that results in accumulation of neonicotinoid residues in the soil surface. Total suspended particulate (TSP) concentrations of neonicotinoid are higher at the edge of fields during planting than tillage of fields. Neonicotinoid residues were detected during wind events, tillage and planting practices. Efforts should be directed towards reducing the amount of material dislodging from treated seeds and reducing the amounts of contaminated dust exhausted by the planter. Avoiding excessive tillage and practicing soil conservation can be useful in minimizing the off-site transport of neonicotinoid residues.

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 score0.879
Threshold uncertainty score0.876

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)0.0000.000

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 head0.027
GPT teacher head0.244
Teacher spread0.218 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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