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Record W4284693691 · doi:10.1680/jgrim.21.00011

Stimulated microbial growth for permeability reductions in granular soils

2022· article· en· W4284693691 on OpenAlexaboutno aff
Mary J. S. Roth, Laurie F. Caslake, Jason T. DeJong, J. D. Greer, Douglas C. Nelson, Rebecca Parales

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Ground Improvement · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Applications in Construction Materials
Canadian institutionsnot available
Fundersnot available
KeywordsPermeability (electromagnetism)DewateringNutrientSoil scienceSoil waterChemistryBacterial growthEnvironmental scienceBacteriaBiologyEcologyGeotechnical engineeringGeologyBiochemistryMembrane

Abstract

fetched live from OpenAlex

The controlled production of microbial growth has the potential to reduce groundwater flow in seepage and dewatering systems. Stimulating the growth of indigenous bacteria could clog the pore space and result in a substantial permeability reduction. This study investigated the spatial distribution of permeability reduction under different nutrient stimulation treatments of indigenous bacteria across 16 cm columns of Ottawa 50–70 sand. Spatially uniform permeability reductions of up to an order of magnitude were achieved using both a high glucose (50 mg/l) and a low glucose (10 mg/l) nutrient formulation. The overall permeability began to decrease by day 2 and approached a minimum permeability by day 14. There was no noticeable difference in the final permeability nor the rate of permeability reduction between high and low glucose formulations. Upscaling of experiments is highly recommended for future studies on the spatial uniformity of microbial growth and biologically induced permeability reductions.

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)0.001
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 score0.023
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.206
Teacher spread0.199 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the Institution of Civil Engineers - Ground ImprovementSame topicMicrobial Applications in Construction MaterialsFrench-language works237,207