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Record W1916944651 · doi:10.5194/amt-8-3789-2015

Synchronous polar winter starphotometry and lidar measurements at a High Arctic station

2015· article· en· W1916944651 on OpenAlexafffundabout
Konstantin Baibakov, N. T. O’Neill, Liviu Ivănescu, T. J. Duck, C. W. Perro, Andreas Herber, Karl‐Heinz Schulz, Otto Schrems

Bibliographic record

VenueAtmospheric measurement techniques · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric aerosols and clouds
Canadian institutionsDalhousie UniversityUniversité de Sherbrooke
FundersEurostarsNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesCanadian Space AgencyFonds Québécois de la Recherche sur la Nature et les TechnologiesCanadian Foundation for Climate and Atmospheric Sciences
KeywordsLidarSun photometerEnvironmental scienceAerosolPhotometerPhotometry (optics)Remote sensingBackscatter (email)Atmospheric sciencesMeteorologyPhysicsStarsGeologyOpticsAstrophysics

Abstract

fetched live from OpenAlex

Abstract. We present recent progress on nighttime retrievals of aerosol and cloud optical properties over the PEARL (Polar Environmental Atmospheric Research Laboratory) station at Eureka (Nunavut, Canada) in the High Arctic (80° N, 86° W). In the spring of 2011 and 2012, a star photometer was employed to acquire aerosol optical depth (AOD) data, while vertical aerosol and cloud backscatter profiles were measured using the CANDAC Raman Lidar (CRL). We used a simple backscatter coefficient threshold (βthr) to distinguish aerosols from clouds and, assuming that aerosols were largely fine mode (FM)/sub-micron, to distinguish FM aerosols from coarse mode (CM)/super-micron cloud or crystal particles. Using prescribed lidar ratios, we computed FM and CM AODs that were compared with analogous AODs estimated from spectral star photometry. We found (βthr dependent) coherences between the lidar and star photometer for both FM events and CM cloud and crystal events with averaged, FM absolute differences being

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.028
GPT teacher head0.236
Teacher spread0.208 · 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.

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

Citations45
Published2015
Admission routes3
Has abstractyes

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