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Record W2092824160 · doi:10.1021/es0343149

Hydrocarbons in Lake Washington Sediments. A 25-Year Retrospective in an Urban Lake

2003· article· en· W2092824160 on OpenAlexafffund
Stuart G. Wakeham, James Forrest, Caroline A. Masiello, Yves Gélinas, Clark Alexander, Peter R. Leavitt

Bibliographic record

VenueEnvironmental Science & Technology · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Regina
FundersNational Research Council Canada
KeywordsEnvironmental chemistryEnvironmental scienceRadiocarbon datingSurface runoffTotal organic carbonOrganic matterHydrocarbonWeatheringIsotopes of carbonDeposition (geology)WatershedPollutionStormwaterContaminationUrban runoffHydrology (agriculture)SedimentGeologyGeochemistryChemistryGeomorphologyPaleontologyEcology

Abstract

fetched live from OpenAlex

Aliphatic and polycyclic aromatic hydrocarbon and stable and radiocarbon isotope distributions are compared for dated cores from the 1970s and 2000 for a 25-year retrospective in Lake Washington, Seattle, WA (USA). Contamination of Lake Washington sediments by petrogenic aliphatic hydrocarbons and pyrolytic polycyclic aromatic hydrocarbons via atmospheric deposition and stormwater runoff peaked between the 1950s and 1970s and has since decreased as stormwater inputs have been reduced. Radiocarbon signatures (delta14C, per 1000) of total organic carbon decrease (increased "age") in the depth interval of highest hydrocarbon concentration. Graphitic black carbon in the year 2000 core showed a historical profile similar to that of the PAH; however high background sediments deposited before the founding of Seattle indicates a considerable nonindustrial component derived from weathering in the watershed. Unlike hydrocarbon contamination, input of terrestrial organic matter (tracked by long-chain fatty alcohols) has increased throughout the late 20th century, documenting a shift in pollutant sources away from hydrocarbons and toward anthropogenic erosion of the region's soils.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.183
Teacher spread0.179 · 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 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

Citations44
Published2003
Admission routes2
Has abstractyes

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