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Record W2089630728 · doi:10.1006/enfo.2001.0064

Preliminary characterization and source assessment of PAHs in tributary sediments of the Athabasca River, Canada

2001· article· en· W2089630728 on OpenAlexaboutno aff
John V. Headley, Christine Akre, F. Malcolm Conly, Kerry M. Peru, Leslie C. Dickson

Bibliographic record

VenueEnvironmental Forensics · 2001
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTributaryOil sandsEnvironmental chemistryHydrocarbonEnvironmental scienceStructural basinGeologyHydrology (agriculture)ChemistryArchaeologyGeomorphologyGeotechnical engineeringGeography

Abstract

fetched live from OpenAlex

The Athabasca Oil Sands are one of four natural oil sands deposits in Northern Alberta, Canada. As a number of new mines are planned in the area, there is a need to establish background levels of natural hydrocarbon release prior to these developments. To this end, various environmental samples were taken from selected tributaries in the oil sands region of the Athabasca River Basin and analysed by gas chromatography/mass spectrometry (GC/MS) for polycyclic aromatic hydrocarbons (PAHs) and their alkylated analogues. Samples were collected over 3 years (1998-2000) to provide an increased understanding of the spatial distribution, nature and extent of natural hydrocarbon release to the environment. Results indicated that levels of total PAHs were elevated in the tributaries (up to 34.7 µg/g) compared to the main stem Athabasca River (<2 µg/g). As expected, samples from the oil sands deposits contained the greatest amounts of PAHs and alkylated PAHs. Profiles of the alkylated PAM distributions were very similar, indicating that all the samples tested were from a common petrogenic source.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations98
Published2001
Admission routes1
Has abstractyes

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