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Record W2993176063 · doi:10.2175/193864704784327403

AN EVALUATION OF SOLID PHASE MICROEXTRACTION FOR ANALYSIS OF ODORANT EMISSIONS FROM STORED BIOSOLIDS CAKE

2004· article· en· W2993176063 on OpenAlexaff
M. Visan, Wayne J. Parker

Bibliographic record

VenueProceedings of the Water Environment Federation · 2004
Typearticle
Languageen
FieldChemical Engineering
TopicOdor and Emission Control Technologies
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiosolidsSolid-phase microextractionEnvironmental chemistryEnvironmental scienceChemistryWaste managementChromatographyGas chromatography–mass spectrometryEnvironmental engineeringMass spectrometryEngineering

Abstract

fetched live from OpenAlex

Odors are a common occurrence at wastewater treatment plants, biosolids processing facilities and biosolids recycling locations. Accurate, objective measurement techniques are needed to monitor emissions, to develop new waste handling procedures and to reduce the production of the volatile gases. The objective of this study was to evaluate the use of solid phase microextraction for measuring common odorants that are found in biosolids facilities. The odorants were collected and concentrated by solid phase microextraction (SPME) and then quantified by gas chromatography with detection by mass spectrometry. A 75-μm Carboxen-Polydimethylsiloxane coating was used for the analysis of trimethylamine, dimethyl sulfide, dimethyl disulfide and methyl mercaptan. Gaseous standards were generated for individual compounds and for dry and wet mixture from permeation apparatus. The differences in sensitivity between fibers, the competition between analytes and water vapor for the active sites on the fiber and the lack of production of artifacts make SPME suited for qualitative analysis and enables quick screening for the identification of compounds with adverse organoleptic characteristics.

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 score0.080
Threshold uncertainty score0.274

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 head0.030
GPT teacher head0.304
Teacher spread0.274 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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