MétaCan
Menu
Back to cohort
Record W2030783642 · doi:10.1021/es020508x

Investigation of the Volatile Organic Substances that Cause the Characteristic Odor of Pentachlorophenol Treated Wood Utility Poles

2002· article· en· W2030783642 on OpenAlexaff
Myriam Fortin, Roland Gilbert, André Besner, Jean-François Labrecque, Joseph Hubert

Bibliographic record

VenueEnvironmental Science & Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicAdvanced Chemical Sensor Technologies
Canadian institutionsUniversité de MontréalHydro-Québec
Fundersnot available
KeywordsOdorPentachlorophenolChemistrySolventVolatile organic compoundChromatographyGas chromatographyEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The nature of the volatile organic compounds that could be at the origin of the characteristic odor of treated wood utility poles was investigated by the study of compositional changes in the chromatographic profiles of fresh-pentachlorophenol (PCP) solvent samples and weathered samples collected from an in-service red pine pole. Over 99 peaks were identified in the chromatogram of the fresh solvent from which a large portion of the C3-, C4-, C5-, C6-alkylbenzene isomers and early eluting n-alkanes was missing from the analysis of weathered samples. Three domains in the chromatographic profile (volatile, semivolatile, and nonvolatile components) were confirmed by assessing the headspace of fresh-PCP solvent samples using direct syringe sampling and solid-phase microextraction. A first level of field validation was achieved using an emission cell for measuring substances emanating from sapwood specimens at different temperatures. The average latent heat of vaporization (deltaHvap) of the PCP-solvent components was estimated at 99.9 kJ/mol from these results. Finally, the analysis of airborne substances at a treating plant and a utility pole storage site confirmed that the C4-, C5-, and C6-alkylbenzene isomers could contribute to the characteristic odor perceived by humans.

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 categoriesScience and technology studies
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 score0.014
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.016
GPT teacher head0.184
Teacher spread0.168 · 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.

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
Published2002
Admission routes1
Has abstractyes

Explore more

Same venueEnvironmental Science & TechnologySame topicAdvanced Chemical Sensor TechnologiesFrench-language works237,207