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Record W2042806696 · doi:10.1100/2012/828343

Fate and Transport of Polycyclic Aromatic Hydrocarbons in Upland Irish Headwater Lake Catchments

2012· article· en· W2042806696 on OpenAlexafffund
Heidi Scott, Julian Aherne, Chris D. Metcalfe

Bibliographic record

VenueThe Scientific World JOURNAL · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsTrent University
FundersUniversity College DublinTrent University
KeywordsChryseneEnvironmental sciencePhenanthreneEnvironmental chemistryPyreneFluorantheneHydrology (agriculture)Soil waterDrainage basinChemistryGeologySoil scienceGeography

Abstract

fetched live from OpenAlex

Polycyclic aromatic hydrocarbons (PAHs) are a concern due to their carcinogenicity and propensity for transboundary atmospheric transport. Ireland is located on the western periphery of Europe and assumed to receive clean Atlantic air. As such, it has been used as an atmospheric reference for comparison to other regions. Nonetheless, few studies have evaluated concentrations of PAHs within the Irish environment. In the current study, PAHs were measured at five upland (500-800 masl) headwater lake catchments in coastal regions around Ireland, remote from industrial point source emissions. Semipermeable membrane devices were deployed in lakes for a 6-month period in July 2009, and topsoils were sampled from each catchment during October 2010. The concentrations of PAHs were low at most study sites with respect to other temperate regions. Homologue groups partitioned between lake and soil compartments based on their molecular weight were: "lighter" substances, such as Phenanthrene and Fluorene, were found in higher proportions in lakes, whereas "heavier" compounds, such as Chrysene and Benz[a]anthracene, were more prominent in soils. Concentrations of PAHs were highest at the east coast sites, potentially due to contributions from historical transboundary and regional combustion sources.

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)0.002
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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 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

Citations34
Published2012
Admission routes2
Has abstractyes

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