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Record W2048366934 · doi:10.1029/2001jd000668

Three‐satellite comparison of polar mesospheric clouds: Evidence for long‐term change

2002· article· en· W2048366934 on OpenAlexaff
E. P. Shettle, Gary E. Thomas, J. J. Olivero, W. F. J. Evans, D. J. Debrestian, L. R. Chardon

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsTrent University
Fundersnot available
KeywordsEnvironmental scienceAtmospheric sciencesExtinction (optical mineralogy)LidarNorthern HemisphereMesosphereRayleigh scatteringPolarSatelliteWavelengthRemote sensingPhysicsGeologyOpticsAstronomyStratosphere

Abstract

fetched live from OpenAlex

Measurements of polar mesospheric clouds (PMCs) from three different satellite instruments are compared. These instruments are the Solar Mesospheric Explorer (SME), the Wind Imaging Interferometer (WINDII), and the Polar Ozone and Aerosol Measurement (POAM II). These measurements have been put on a common basis, correcting for differences in the wavelengths and measurement techniques used. This common basis is the probability distribution of the excess extinction ratio (EER) at a standard wavelength of 265 nm, where the EER is the ratio of the PMC extinction coefficient to the background molecular Rayleigh scattering coefficient. The results indicate that the POAM and WINDII measurements in the Southern Hemisphere had a higher probability of observing bright PMCs during the 1993–1996 time period than SME did a decade earlier in 1983–1986. Local time variations identified in WINDII data are interpreted in terms of a diurnal and semidiurnal component of average EER. These results are qualitatively similar to those found from lidar soundings of noctilucent cloud at sites in Norway and at the South Pole. Differences in interannual variability, local time of the measurements, assumed particle size distributions, and solar cycle effects are ruled out as possible explanations of the differences.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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 score0.323
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.196
GPT teacher head0.381
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 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

Citations42
Published2002
Admission routes1
Has abstractyes

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