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Record W2015302898 · doi:10.1029/2008gl033733

Occurrence of weak, sub‐micron, tropospheric aerosol events at high Arctic latitudes

2008· article· en· W2015302898 on OpenAlexafffundabout
Norman T. O’Neill, Ovidiu Pancrati, Konstantin Baibakov, E. W. Eloranta, R. L. Batchelor, J. Freemantle, L. J. B. McArthur, Kimberly Strong, R. Lindenmaier

Bibliographic record

VenueGeophysical Research Letters · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsUniversity of TorontoEnvironment and Climate Change CanadaUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaNational Oceanic and Atmospheric AdministrationFonds Québécois de la Recherche sur la Nature et les TechnologiesCanadian Foundation for Climate and Atmospheric SciencesNational Aeronautics and Space Administration
KeywordsAerosolLidarEnvironmental scienceTroposphereArcticClimatologyAtmospheric sciencesObservatoryRemote sensingThe arcticLatitudeMeteorologyGeologyGeographyOceanographyAstronomyPhysics

Abstract

fetched live from OpenAlex

Numerous fine mode (sub‐micron) aerosol optical events were observed during the summer of 2007 at the High Arctic atmospheric observatory (PEARL) located at Eureka, Nunavut, Canada. Half of these events could be traced to forest fires in southern and eastern Russia and the Northwest Territories of Canada. The most notable findings were that (a) a combination of ground‐based measurements (passive sunphotometry, high spectral resolution lidar) could be employed to determine that weak (near sub‐visual) fine mode events had occurred, and (b) this data combined with remote sensing imagery products (MODIS, OMI‐AI, FLAMBE fire sources), Fourier transform spectroscopy and back trajectories could be employed to identify the smoke events.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.003

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 head0.023
GPT teacher head0.270
Teacher spread0.247 · 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; both teacher heads agree on what is shown here.

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

Citations35
Published2008
Admission routes3
Has abstractyes

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