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Record W2113716593 · doi:10.1139/s08-037

Soil type effects on petroleum contamination characterization using ultraviolet induced fluorescence excitation-emission matrices (EEMs) and parallel factor analysis (PARAFAC)

2008· article· en· W2113716593 on OpenAlexafffundvenue
M. Alostaz, Kevin W. Biggar, Robert Donahue, Gregory J. Hall

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2008
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Chemometric Analyses
Canadian institutionsCanadian Natural Resources
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleumSoil waterFluorescenceHydrocarbonMatrix (chemical analysis)Environmental chemistryChemistrySoil contaminationContaminationUltravioletEnvironmental sciencePorosityAnalytical Chemistry (journal)Soil scienceMaterials scienceChromatographyEcologyOrganic chemistry

Abstract

fetched live from OpenAlex

The ultraviolet induced fluorescence signatures of various petroleum products were evaluated in different soils to examine the impact of soil type, grain size, porosity, and mineralogy. The different soil matrices induced changes to the spectral features of petroleum hydrocarbon fluorescence excitation-emission matrices (EEMs). Once the effect of the soil matrix was characterized, fluorescence EEMs were analyzed using parallel factor analysis (PARAFAC) and soft independent method of class analogy (SIMCA) to identify the petroleum products and their underlying aromatic hydrocarbon components. For quantitative analysis, total fluorescence values obtained from fluorescence EEMs of analyzed petroleum products were used to estimate their concentrations in different soil matrices. Results indicated that this approach provides identifying fingerprinting and reasonable estimate of concentrations for a number of petroleum products in different soils matrices.

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 score0.577
Threshold uncertainty score0.384

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 communication0.0000.000
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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations20
Published2008
Admission routes3
Has abstractyes

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