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Record W2750048143 · doi:10.5194/acp-18-3485-2018

Temporally delineated sources of major chemical species in high Arctic snow

2018· article· en· W2750048143 on OpenAlexafffundabout
Katrina M. Macdonald, Sangeeta Sharma, Desiree Toom, Alina Chivulescu, Andrew W.G. Platt, Mike Elsasser, Lin Huang, W. R. Leaitch, Nathan Chellman, Joseph R. McConnell, Heiko Bozem, Daniel Kunkel, Ying Duan Lei, Cheol–Heon Jeong, Jonathan P. D. Abbatt, Greg J. Evans

Bibliographic record

VenueAtmospheric chemistry and physics · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British ColumbiaEnvironment and Climate Change CanadaUniversity of TorontoGovernment of Ontario
KeywordsSnowSea saltArcticSulfateEnvironmental scienceAerosolAtmospheric sciencesHazeCarbon blackSea sprayEnvironmental chemistryEnrichment factorOceanographyClimatologyChemistryGeologyMeteorologyHeavy metalsGeography

Abstract

fetched live from OpenAlex

Abstract. Long-range transport of aerosol from lower latitudes to the high Arctic may be a significant contributor to climate forcing in the Arctic. To identify the sources of key contaminants entering the Canadian High Arctic an intensive campaign of snow sampling was completed at Alert, Nunavut, from September 2014 to June 2015. Fresh snow samples collected every few days were analyzed for black carbon, major ions, and metals, and this rich data set provided an opportunity for a temporally refined source apportionment of snow composition via positive matrix factorization (PMF) in conjunction with FLEXPART (FLEXible PARTicle dispersion model) potential emission sensitivity analysis. Seven source factors were identified: sea salt, crustal metals, black carbon, carboxylic acids, nitrate, non-crustal metals, and sulfate. The sea salt and crustal factors showed good agreement with expected composition and primarily northern sources. High loadings of V and Se onto Factor 2, crustal metals, was consistent with expected elemental ratios, implying these metals were not primarily anthropogenic in origin. Factor 3, black carbon, was an acidic factor dominated by black carbon but with some sulfate contribution over the winter-haze season. The lack of K+ associated with this factor, a Eurasian source, and limited known forest fire events coincident with this factor's peak suggested a predominantly anthropogenic combustion source. Factor 4, carboxylic acids, was dominated by formate and acetate with a moderate correlation to available sunlight and an oceanic and North American source. A robust identification of this factor was not possible; however, atmospheric photochemical reactions, ocean microlayer reaction, and biomass burning were explored as potential contributors. Factor 5, nitrate, was an acidic factor dominated by NO3−, with a likely Eurasian source and mid-winter peak. The isolation of NO3− on a separate factor may reflect its complex atmospheric processing, though the associated source region suggests possibly anthropogenic precursors. Factor 6, non-crustal metals, showed heightened loadings of Sb, Pb, and As, and correlation with other metals traditionally associated with industrial activities. Similar to Factor 3 and 5, this factor appeared to be largely Eurasian in origin. Factor 7, sulfate, was dominated by SO42− and MS with a fall peak and high acidity. Coincident volcanic activity and northern source regions may suggest a processed SO2 source of this factor.

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 score0.207
Threshold uncertainty score0.412

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)0.0010.000

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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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

Citations16
Published2018
Admission routes3
Has abstractyes

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