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Record W2047335062 · doi:10.1002/rem.20079

Nanotechnology and groundwater remediation: A step forward in technology understanding

2006· article· en· W2047335062 on OpenAlexaboutno aff
Christian Macé, Steve Desrocher, Florin Gheorghiu, Allen Kane, M. PUPEZA, Miroslav Černík, Petr Kvapil, R. Venkatakrishnan, Wei‐xian Zhang

Bibliographic record

VenueRemediation Journal · 2006
Typearticle
Languageen
FieldEngineering
TopicEnvironmental remediation with nanomaterials
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental remediationGroundwaterContaminated groundwaterGroundwater remediationEnvironmental scienceZerovalent ironHydrogeologyNanotechnologyBiochemical engineeringEngineeringContaminationMaterials scienceChemistryEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Nanotechnology application to contaminated site remediation, and especially the use of nanoscale zero‐valent iron particles to treat volatile organic compound (VOC)‐impacted groundwater, is now recognized as a promising solution for cost‐effective in situ treatment. Results obtained during numerous pilot tests undertaken by Golder Associates between 2003 and 2005 in North America (United States and Canada) and Europe have been used to present a synthetic cross‐comparison of technology dynamics. The importance of a comprehensive understanding of the site‐specific geological, hydrogeological, and geochemical conditions, the selection of appropriate nanoscale particles, the importance of monitoring geochemical parameters during technology application, and the potential of nanoparticle impact on microbial activity are discussed in this article. The variable technology dynamics obtained during six pilot tests (selected among numerous other tests) are then presented and discussed. © 2006 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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.005
Open science0.0000.001
Research integrity0.0020.001
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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations73
Published2006
Admission routes1
Has abstractyes

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