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Record W2594322755 · doi:10.1002/rem.21511

The Use of Zero‐Valent Iron (ZVI) Technology to Promote DDT and Dieldrin Degradation at Point Pelee National Park

2017· article· en· W2594322755 on OpenAlexafffund
Carolina P. Dahmer, Allison Rutter, Barbara A. Zeeb

Bibliographic record

VenueRemediation Journal · 2017
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsQueen's UniversityRoyal Military College of Canada
FundersParks Canada
KeywordsDieldrinZerovalent ironEnvironmental remediationPesticideEnvironmental chemistryEnvironmental scienceGroundwaterNational parkPesticide degradationContaminationSoil contaminationDegradation (telecommunications)Environmental engineeringSoil waterEnvironmental protectionChemistryEcologySoil scienceGeologyBiologyEngineering

Abstract

fetched live from OpenAlex

Point Pelee National Park (PPNP) is highly contaminated with dichlorodiphenyltrichloroethane (DDT) and dieldrin due to the historical use of these two persistent organochlorine pesticides. Zero‐valent iron (ZVI) technology with and without amendments has been successfully used in the past to promote organochlorine pesticides degradation in several locations in North America and Europe. In this study, the use of two commercially available ZVI products, DARAMEND ® and EHC ® , to promote DDT and dieldrin degradation in PPNP's soil and groundwater were investigated. DARAMEND ® was applied to PPNP's soil in a laboratory experiment and in an in situ pilot‐scale plot. In both cases, DARAMEND ® did not significantly increase DDT or dieldrin degradation in treated soils. The effectiveness of EHC ® was tested in a laboratory experiment that simulated the park's groundwater environment using PPNP's pesticide contaminated soil. The result was consistent with the one reported for DARAMEND ® , in that there was no significant increase in DDT or dieldrin degradation in any of the samples treated with EHC ® . These results demonstrate that both of these ZVI commercially available products are not suitable for in situ remediation at PPNP. ©2017 Wiley Periodicals, Inc.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.440
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.244
Teacher spread0.223 · 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 designBench or experimental
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

Citations4
Published2017
Admission routes2
Has abstractyes

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