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Record W2004448717 · doi:10.1177/0734242x0202000510

Calcium hypochlorite removal of mercury and petroleum hydrocarbons from co-contaminated soils

2002· article· en· W2004448717 on OpenAlexaff
Adrian J. Renneberg, M. J. Dudas

Bibliographic record

VenueWaste Management & Research The Journal for a Sustainable Circular Economy · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMercury (programming language)Environmental chemistrySoil waterHypochloriteChemistryPetroleumCalcium hypochloriteSoil contaminationContaminationMineral oilOrganic matterWaste managementEnvironmental scienceChlorineInorganic chemistrySoil scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Former oil and gas processing sites are often contaminated with both mercury and petroleum hydrocarbons. Traditional methods for treating these co-contaminated soils include thermal treatments and placement in landfills (Stepan et al. 1993). This study examines a chemical oxidation treatment, calcium hypochlorite, and its effectiveness in reducing both the petroleum and mercury content of several industrial soils. A sequential extraction method was used to determine the forms of mercury removed through the hypochlorite treatment. The forms included five organic forms of mercury, mercury associated with the mineral phase, ion exchangeable mercury, water phase mercury, and mercury associated with the petroleum hydrocarbons. The hypochlorite treatment did lower the mercury content of all five soils, but failed to remove the petroleum hydrocarbons from the soils. Prior to the hypochlorite treatment the dominant form of mercury was that associated with soil organic matter while after treatment most of the mercury remaining in the soil was associated with either the petroleum hydrocarbons or the mineral phase.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

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 head0.041
GPT teacher head0.302
Teacher spread0.261 · 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 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

Citations7
Published2002
Admission routes1
Has abstractyes

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