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Record W2734584621 · doi:10.1080/15230430.2017.1415854

Seasonal and decadal variability of dust observations in the Kangerlussuaq area, west Greenland

2018· article· en· W2734584621 on OpenAlexaboutno aff
Joanna E. Bullard, Tom Mockford

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
FundersNatural Environment Research CouncilSight Research UKLeverhulme Trust
KeywordsGreenland ice sheetAeolian processesEnvironmental scienceMineral dustGroenlandiaCryosphereClimatologyIce coreDeposition (geology)Asian DustPhysical geographyGeologyOceanographySedimentSea iceAtmospheric sciencesIce sheetAerosolGeographyMeteorologyGeomorphology

Abstract

fetched live from OpenAlex

Dust emissions from high-latitude, cold-climate environments have started receiving more attention in the past decade. This is because emission frequency and magnitudes are expected to increase with rising global temperatures, leading to a reduction in terrestrial ice masses and increases in suitable sediment for the aeolian system. Of the identified high-latitude dust source regions, Greenland has received relatively little attention. Using World Meteorological Organization (WMO) dust-code analysis, this study presents a seventy-year record of dust events and preferential dust transport pathways from Kangerlussuaq, west Greenland. A clear seasonal pattern of dust emissions shows increases in dust events in spring and autumn driven by effective winds and sediment supply. The decadal record suggests an increase in the magnitude, but not frequency, of dust events since the early 1990s. Pathways analysis suggests that dust is preferentially transported away from the Greenland Ice Sheet (GrIS) toward the Davis Strait and Labrador Sea. When dust is transported toward the GrIS, it is more likely to be deposited in the ice-marginal ablation zone than on the higher altitude areas of the ice sheet. The impact of dust deposition on terrestrial, cryospheric, and aquatic environments is also discussed.

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 score0.197
Threshold uncertainty score0.391

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 studies0.0000.000
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 head0.093
GPT teacher head0.311
Teacher spread0.218 · 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

Citations76
Published2018
Admission routes1
Has abstractyes

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