MétaCan
Menu
Back to cohort
Record W2796171069 · doi:10.11159/awspt18.133

Lysimeter Leaching Study of Cyantraniliprole

2018· article· en· W2796171069 on OpenAlexvenueno aff
В. Н. Колупаева, A. A. Belik, А. А. Кокорева

Bibliographic record

VenueProceedings of the World Congress on Civil, Structural, and Environmental Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsnot available
Fundersnot available
KeywordsLysimeterLeaching (pedology)Environmental scienceSoil scienceSoil water

Abstract

fetched live from OpenAlex

Modern agriculture is impossible without the use of plant protection products.In addition to the expected effect of protection from pests, diseases and weeds, the use of pesticides often has an adverse effect on the environment and nontarget organisms.In particular, numerous studies have revealed the discovery of pesticide residues in groundwater around the world.For regulation purpose, in order to assess pesticide environmental risks in Russia, the experimental data obtained in the EU is used.The climate in Russia is colder with significantly higher water percolation than in Europe, which leads to a higher risk of pesticide migration to groundwater.This is also facilitated by the movement of plant protection products with preferential water flows through macropores and cracks.The purpose of the research was to study the migration of the insecticide cyantraniliprole in the soil profile.Cyantraniliprole is a moderately persistence (DT 50 =34.4days) medium-mobility (К ос =241) substance.The experiment was carried out at the lysimeters of the Soil Research Station of Moscow State University from June 2015 to May 2017.The soil of lysimeter is soddy-podzolic silt loam.The insecticide was used at the recommended (0.4 kg ha -1 ) and tenfold rates in June 2015 and then in June 2016.Water leachate from lysimeter was collected every week.Soil samples were collected every 5 cm till depth of 50 cm in spring and autumn.Cyantraniliprole was analysed by HPLC.Detection limits of the analytical method was 0.5 μg / L and 2.5 μg / kg for water and soil respectively.The average annual air temperature in the years of the experiment was close to the average long-term values.The amount of precipitation in 2016 exceeded the mean annual value by 90 mm, and in the summer period of 2016 -by 121 mm.In 2015, average precipitation values for the year and for the seasons were close to the average annual parameters.However, in the summer of 2015 heavy showers with a daily rainfall rate exceeding a quarter of the monthly norm were observed.The maximum depth of migration of cyananthraniliprole in the soil profile was 35 cm in October 2015 and 40 cm in October 2016.Despite the fact that cyanantraniliprole is a medium-persistence, one year after the second treatment (in May 2017), a rather large concentration of the pesticide (near 35% of applied rate) was in the soil, which distributed to a depth of 30 cm, with a maximum in the upper 5-cm layer.Cyantraniliprole was found in the leachate of lysimeter water 2 weeks after first application in both lysimeters (with the recommended and tenfold doses), the pesticide concentrations were 0.8 and 1.5 μg / L respectively.This was facilitated by the precipitation of several showers with rainfall more than 20 mm.This indicates a high mobility of the pesticide in this soil and a large influence of rainfall on the rapid arrival of the pesticide beyond the soil profile.Cyantraniliprole was found in most of the analyzed water samples.The pesticide in the water leachate wasn't found in 2015 in 22% of the samples at the recommended dose and in 6% of the samples at a tenfold dose.In 2016 and 2017, cyantraniliprole was detected in all the selected aqueous samples.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 head0.004
GPT teacher head0.174
Teacher spread0.170 · 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 source (direct Gemma or distilled Codex), 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Civil, Structural, and Environmental EngineeringSame topicElectrochemical sensors and biosensorsFrench-language works237,207