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Record W2027753001 · doi:10.1139/s04-054

Reduction of hydraulic conductivity changes in an in-ground bioreactor

2005· article· en· W2027753001 on OpenAlexvenueno aff
Gioseph Anello, Philippe Lamarche, Jean A Héroux

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial bioremediation and biosurfactants
Canadian institutionsnot available
FundersDirectorate-General for the Environment
KeywordsHydraulic conductivityNaphthaleneBioreactorBiodegradationBiomass (ecology)Volume (thermodynamics)ConductivityChemical engineeringHydraulic retention timeBioremediationMaterials scienceEnvironmental engineeringChemistryEnvironmental chemistryWastewaterEnvironmental scienceSoil waterSoil scienceGeologyOrganic chemistry

Abstract

fetched live from OpenAlex

The biodegradation of dissolved naphthalene was studied in the laboratory, in preparation for a field-scale investigation of enhanced bioremediation in a permeable barrier. The laboratory experiments were performed, under water saturated conditions, in acrylic columns packed with silica sand. The sand contained 1% by volume of granular activated carbon (GAC). Naphthalene, nutrient, and nitrate solutions were pumped through, and concentrations and liquid pressures were monitored. One objective of the laboratory study was to determine if the decrease in hydraulic conductivity caused by microbial growth could be reduced. It was believed that biomass would attach preferentially to the small proportion of GAC added to the sand. Scanning electron microscope micrographs showing evidence of preferential attachment of biomass on the GAC are presented in this paper. A calculation indicates that the decrease in hydraulic conductivity could be less than 6%. Data gathered to design the field scale bioreactor and the design and assessment of the performance of the permeable barrier are presented elsewhere. Key words: hydraulic conductivity, biomass growth, in-situ bioreactor, granular activated carbon, naphthalene.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.232

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 head0.010
GPT teacher head0.206
Teacher spread0.196 · 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

Citations6
Published2005
Admission routes1
Has abstractyes

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