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Record W4393317171 · doi:10.1111/gwmr.12643

Bioremediation of Chlorate and Chromium in Soil Columns Using Contaminated Site Native Culture

2024· article· en· W4393317171 on OpenAlexfundaboutno aff
Mehdi Motevasselin, Beata Gorczyca, Indra Kalinovich, Richard Sparling, Ramanathan Sri Ranjan

Bibliographic record

VenueGroundwater Monitoring & Remediation · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicChemical Analysis and Environmental Impact
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBioremediationChromiumChlorateContaminationEnvironmental chemistrySoil contaminationEnvironmental scienceChemistryBiologyEcologyInorganic chemistry

Abstract

fetched live from OpenAlex

Abstract Chlorate and hexavalent chromium (chromate) are both widely used in different industries, and the improper waste management in the past left many sites with elevated concentrations in groundwater that pose potential risk to human and/or ecological health. Bioremediation is a sustainable management solution that can reduce both of these contaminants to less toxic species. In our earlier microcosms experiments, we have demonstrated that native microorganisms collected from a site contaminated with chlorate and chromate can lower the concentration of these chemicals in groundwater to acceptable regulatory levels provided sufficient electron donor, nitrogen, and phosphorous are provided. In this study, continuous flow column experiments were performed using soil from the site impacted by both chlorate and chromate in the Province of Manitoba (Canada) and synthetic groundwater amended with acetate, nitrogen, and phosphorous. The objective was to evaluate at a bench scale possibility of in‐situ groundwater treatment. Concentrations of chromate and chlorate measured in the columns' effluent water dropped by 86% and 96%, respectively. However, increased biomass and precipitation of trivalent chromium reduced the water flow rate in the columns, a concern for implementing this method as a long‐term in‐situ remediation solution.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score0.443

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 communication0.0000.001
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 head0.013
GPT teacher head0.242
Teacher spread0.229 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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