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Record W4312126800 · doi:10.1029/2022gl101496

Presence, Sources and Transport of Polycyclic Aromatic Hydrocarbons in the Arctic Ocean

2022· article· en· W4312126800 on OpenAlexaffabout
Lihong Zhang, Yuxin Ma, Šimon Vojta, Maya Morales‐McDevitt, Mario Hoppmann, Thomas Soltwedel, Jane L. Kirk, Amila O. De Silva, Derek C. G. Muir, Rainer Lohmann

Bibliographic record

VenueGeophysical Research Letters · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change Canada
FundersNational Institutes of HealthNational Institute of General Medical SciencesNational Natural Science Foundation of China
KeywordsArcticBiogeochemical cycleEnvironmental scienceOceanographySeawaterArchipelagoThe arcticEnvironmental chemistryParticulatesSurface waterGeologyChemistryEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract Polycyclic aromatic hydrocarbons (PAHs) are continuously released from multiple sources and are prone to long‐range transport. Little is known regarding their presence, transport and fate in remote and deep oceans. Polyethylene passive samplers were hence deployed at deep moorings and surface seawater in the Fram Strait and Canadian Archipelago, as well as in air and surface water of the lower Great Lakes, a potential high‐emission region, to understand the transport of PAHs to the Arctic. Dissolved PAHs showed significantly higher concentrations in the lower Great Lakes than those in the high Arctic. Concentrations of dissolved PAHs (Σ19PAHs) ranged from 33 to 300 pg/L in the Fram Strait; the vertical profiles generally exhibited a decreasing trend toward deep waters, which was potentially influenced by hydrological and biogeochemical processes. PAHs were exported from the Arctic Ocean to the North Atlantic through the Fram Strait and the Davis Strait.

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 score0.179
Threshold uncertainty score0.357

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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

Citations20
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueGeophysical Research Letters→Same topicToxic Organic Pollutants Impact→French-language works237,207→