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Record W2887055950

Phytoremediation of Pesticide Residues in Southwestern Ontario

2018· dissertation· en· W2887055950 on OpenAlexaboutno aff
Jinghan Zhao

Bibliographic record

VenueThe Atrium (University of Guelph) · 2018
Typedissertation
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhytoremediationPesticidePesticide residueEnvironmental scienceEnvironmental chemistryGeographyHeavy metalsAgronomyBiologyChemistry
DOInot available

Abstract

fetched live from OpenAlex

This study aims to improve the Ontario ginseng quality by reducing the DDT concentration in the soil through phytoremediation technology. An intensified DDT detection method was developed for the specific sandy soil used for ginseng cultivation. This method is based on the conventional detection method from EPA guidelines and combined drying process, ultrasonic probe extraction process and rotary concentration process. The intensified detection method significantly enhanced the results accuracy, and reduced the time consumption and labour requirement. The DDT residue distribution in agricultural soil located in the southwestern Ontario was also investigated to determine the degradation level during the past 52 years. The results of phytoremediation in the lab scale showed that DDT was successfully translocated to the aerial tissues. It can be concluded that the phytoremediation could be an effective remediation technology of DDT contaminated soil in the southwestern Ontario.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.601
Threshold uncertainty score0.997

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)0.0040.000

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 head0.013
GPT teacher head0.206
Teacher spread0.194 · 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.

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
Published2018
Admission routes1
Has abstractyes

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