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Occurrence, characterization, source, and risk assessment of petroleum-related hydrocarbons in sediments along St. Clair River, Ontario, Canada

2021· article· en· W3207564218 on OpenAlexafffundabout
Zeyu Yang, Keval Shah, Sonia Laforest, Claire Courtemanche, William Durand, Patrick Lambert, Bruce P. Hollebone, Carl E. Brown, Michael Goldthorp, Kevin Watson, Chun Yang, Diane Dey, Vanessa Beaulac

Bibliographic record

VenueMarine Pollution Bulletin · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change Canada
FundersGovernment of Canada
KeywordsPhenanthreneSedimentEnvironmental chemistryPetroleumEnvironmental scienceAcenaphthyleneBenthic zoneHydrology (agriculture)GeologyChemistryOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Total petroleum hydrocarbons (TPH), n-alkanes, petroleum biomarkers, and polycyclic aromatic hydrocarbons (PAHs) were analyzed in the sediments collected from the shorelines and bottom of St. Clair River, Ontario, Canada. Most of the sampling sites had low TPH (< 20 μg/g). River bottom sediment usually had higher level of TPHs, total alkanes, total biomarkers, and total PAHs than most of the shoreline ones. Mixed biogenic and petrogenic n-alkanes were present in all the sites. Most sites had trace amounts of petroleum biomarkers. Mixed pyrogenic and petrogenic inputs with the predominant petroleum, have contributed to the detected PAHs at all sampling sites. PAHs detected would not show potential toxicity to benthic organisms in all shoreline sampling sites; however, some light molecular weight PAHs (e.g., phenanthrene, 2-methyl naphthalene, and acenaphthylene) are anticipated to have possible adverse impacts to sediment-dwelling organisms in part of the river bottom sediment.

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 score0.019
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
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 head0.003
GPT teacher head0.189
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

Citations5
Published2021
Admission routes3
Has abstractyes

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