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Record W2525694283 · doi:10.1002/2016gl070952

Peat bogs in northern Alberta, Canada reveal decades of declining atmospheric Pb contamination

2016· article· en· W2525694283 on OpenAlexafffundabout
William Shotyk, P. G. Appleby, Beatriz Bicalho, Lauren J. Davies, Duane Froese, Iain Grant‐Weaver, Michael Krachler, Gabriel Magnan, Gillian Mullan‐Boudreau, Tommy Noernberg, Rick Pelletier, Bob Shannon, Simon van Bellen, Claudio Zaccone

Bibliographic record

VenueGeophysical Research Letters · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of AlbertaAlberta Environment and Parks
KeywordsBogPeatOmbrotrophicEnvironmental scienceMacrofossilDeposition (geology)Atmosphere (unit)ContaminationPhysical geographyEnvironmental chemistryHydrology (agriculture)GeologyOceanographySedimentEcologyArchaeologyGeographyGeomorphologyChemistryHoloceneMeteorology

Abstract

fetched live from OpenAlex

Abstract Peat cores were collected from six bogs in northern Alberta to reconstruct changes in the atmospheric deposition of Pb, a valuable tracer of human activities. In each profile, the maximum Pb enrichment is found well below the surface. Radiometric age dating using three independent approaches ( 14 C measurements of plant macrofossils combined with the atmospheric bomb pulse curve, plus 210 Pb confirmed using the fallout radionuclides 137 Cs and 241 Am) showed that Pb contamination has been in decline for decades. Today, the surface layers of these bogs are comparable in composition to the “cleanest” peat samples ever found in the Northern Hemisphere, from a Swiss bog ~ 6000 to 9000 years old. The lack of contemporary Pb contamination in the Alberta bogs is testimony to successful international efforts of the past decades to reduce anthropogenic emissions of this potentially toxic metal to the atmosphere.

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)0.001
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 score0.298
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.273
Teacher spread0.252 · 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

Citations79
Published2016
Admission routes3
Has abstractyes

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