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The impact of permanganate on the ability of granular iron to degrade trichloroethene

2005· article· en· W1997457040 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGroundwater Monitoring & Remediation · 2005
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPermanganateDegradation (telecommunications)Vinyl chlorideIron oxideChemistryChemical engineeringPotassium permanganateChlorideInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The primary goal of this study was to investigate the influence of permanganate (MnO 4 − ) on the performance of granular iron permeable reactive barriers. The degradation of trichloroethene (TCE) was measured initially and then over time as a MnO 4 − solution was passed through laboratory columns packed with granular iron. Concentration profiles for MnO 4 − , TCE, and degradation products (dichloroethene isomers and vinyl chloride), as well as pH, were observed. The pH increased sharply after passing MnO 4 − through the column, from ~8 to 11. MnO 4 − rapidly oxidized the granular iron and formed insoluble precipitates and oxide films or coatings on the granular iron surfaces. The precipitates did not accumulate in sufficient quantity to cause a measurable decline in hydraulic conductivity; however, the surface films formed as a consequence of the addition of MnO 4 − caused the iron to become nonreactive with respect to both MnO 4 − and TCE.

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.

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)0.001
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 score0.347
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.014
GPT teacher head0.245
Teacher spread0.231 · 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