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Record W1987367160 · doi:10.1680/wama.2008.161.6.375

Assessing performance of a permeable biobarrier

2008· article· en· W1987367160 on OpenAlexaff
Ryan Wilson, Wai-lim Yip, Claudia Naas

Bibliographic record

VenueProceedings of the Institution of Civil Engineers - Water Management · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicGroundwater flow and contamination studies
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPlumeEnvironmental scienceMetric (unit)Flux (metallurgy)Mass fluxGroundwaterBTEXContaminationGroundwater contaminationSoil scienceGeologyMeteorologyChemistryGeotechnical engineeringEngineeringAquiferMechanicsEthylbenzene

Abstract

fetched live from OpenAlex

While the past 15 years have seen rapid development of permeable reactive barriers and other in situ systems for the treatment of groundwater contamination, assessing performance has not received the same attention. In some cases the long-screened monitoring wells installed as part of initial site investigations are used to monitor treatment, while in others a few additional wells may be installed to serve as sentinels. Performance is assessed based on differences in contaminant concentrations, which is unreliable where plumes are spatially complex and/or temporally unstable. In this work, a standard concentration performance metric is compared with that based on mass flux, using data collected during an evaluation of a discontinuous aerobic biobarrier treating a near-source (and spatially complex) BTEX plume. The data were derived from high-resolution multilevel samplers installed up- and down-gradient of the treatment system. Concentrations in screened monitoring wells of various lengths were approximated by interval-weighted concentrations from these multilevels. The accuracy of the different performance assessment methods was measured based on estimated uncertainty generated as a result of observed spatial plume complexity. While more data intensive, mass flux proved to be a more reliable performance metric. Assessment using data from few long-screened wells could not reliably match that of the flux fences, but did come within acceptable limits when the number of wells approached the number of multilevels.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score0.266

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 head0.011
GPT teacher head0.193
Teacher spread0.181 · 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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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