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Record W2019133351 · doi:10.1029/2004jd004958

Chemical size distributions of boundary layer aerosol over the Atlantic Ocean and at an Antarctic site

2006· article· en· W2019133351 on OpenAlexfundno aff
Aki Virkkula, Kimmo Teinilä, Risto Hillamo, Veli‐Matti Kerminen, Sanna Saarikoski, Minna Aurela, Ismo Kalevi Koponen, Markku Kulmala

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric chemistry and aerosols
Canadian institutionsnot available
FundersAlberta Agricultural Research Institute
KeywordsSea saltSulfateAerosolChlorideNitrateSalinityOceanographyAtmospheric sciencesGeologyMineralogyChemistry

Abstract

fetched live from OpenAlex

Chemical size distributions of aerosols were measured using a 12‐stage low‐pressure impactor over the Atlantic Ocean in November–December 1999 and at an Antarctic site, Aboa, in January 2000. In the polluted latitudes north of the equator, particles were neutral in all sizes but accumulation mode was acidic in the clean areas. Chloride depletion was analyzed in detail. In most areas, chloride depletion could be explained by replacement by sulfate, nitrate, and methane sulfonate. In Antarctica the chloride losses increased in all size ranges and the contribution of each anion to the chloride depletion varied with increasing air mass residence time over continental Antarctica. The modal structure of the size distributions of ionic compounds was analyzed. The modes derived from chemical mass size distributions were compared with modes obtained from number size distributions. The modes of non‐sea‐salt sulfate and MSA were very similar suggesting that these particles were internally mixed. The nitrate mass modes were closer to the sea salt surface modes than the sea salt mass modes, suggesting that nitrate was on the surface of sea salt particles.

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 score0.042
Threshold uncertainty score0.083

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 head0.017
GPT teacher head0.273
Teacher spread0.256 · 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

Citations66
Published2006
Admission routes1
Has abstractyes

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