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Record W2592664913 · doi:10.1038/srep43622

Size-resolved Pb distribution in the Athabasca River shows snowmelt in the bituminous sands region an insignificant source of dissolved Pb

2017· article· en· W2592664913 on OpenAlexafffund
Chad W. Cuss, Iain Grant‐Weaver, William Shotyk

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversity of Alberta
FundersCanada's Oil Sands Innovation AllianceUniversity of Alberta
KeywordsSnowmeltOil sandsEnvironmental scienceParticulatesWeatheringSeawaterErosionEnvironmental chemistryPollutionGeochemical cycleHydrology (agriculture)GeologyAsphaltGeochemistrySnowChemistryOceanographyGeomorphologyEcology

Abstract

fetched live from OpenAlex

Abstract Lead (Pb) is a metal of special importance because of its long history of commercial and industrial use, global atmospheric contamination accelerated by the use of gasoline additives, and health effects, with children being especially vulnerable. Global atmospheric Pb pollution reached its zenith in the 1970’s, but subsequent impacts on freshwater aquatic systems are poorly understood. Employing metal-free sampling and handling protocols, we show that snowmelt from the Athabasca bituminous sands region is an insignificant source of dissolved Pb to the Athabasca River (AR). Total Pb in the AR is low, and almost entirely in particulate form. Lead in the suspended solids in the AR exactly follows thorium (Th), a conservative lithophile element, and a linear regression of Pb against Th (Pb = 1.6 × Th + 0.0; R 2 = 0.99) yields a slope identical to the Pb/Th ratio in the Upper Continental Crust. In the “dissolved” fraction, the Pb/Th ratio is equivalent to that of deep, open ocean seawater; and dominated by colloidal forms. Taken together, these results show that the efforts of recent decades to reduce anthropogenic Pb to the environment have been successful: Pb loading to the river can now be explained predominantly by natural processes, namely erosion plus chemical weathering.

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)0.007
metaresearch head score (Gemma)0.001
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 score0.153
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.024
GPT teacher head0.256
Teacher spread0.232 · 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

Citations45
Published2017
Admission routes2
Has abstractyes

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