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Record W2010464309 · doi:10.1139/p02-024

Studies for the Odin sub-millimetre radiometer: I. Radiative transfer and instrument simulation

2002· article· en· W2010464309 on OpenAlexvenueno aff
Patrick Eriksson, Frank Merino, D. Murtagh, Philippe Baron, Philippe Ricaud, J. de La Noë

Bibliographic record

VenueCanadian Journal of Physics · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsPhysicsRadiative transferWeightingDepth soundingRadiometerRemote sensingAtmospheric physicsMillimeterAtmospheric radiative transfer codesOpticsMeteorologyAtmosphere (unit)Acoustics

Abstract

fetched live from OpenAlex

The Odin satellite mission will include radiometric measurements of the Earth's atmosphere in a limb-sounding mode, using frequencies between 480 and 580 GHz, with the overall aim of retrieving vertical distributions of atmospheric constituents. The current paper, being one of a three-part series, addresses primarily the modelling of atmospheric radiative transfer and the effect of instrumental properties: the forward model. Such a model is required for the retrieval process and this presentation puts emphasis on refraction, sensor characteristics, systematic model errors, and some implementation aspects. Refraction must be considered below about 15 km and an efficient algorithm to include this effect is presented. Sensor parts treated are the antenna, the side-band filter, and the spectrometer. The forward model is also essential for determining the needed weighting functions. A semi-analytical expression for species-abundance weighting functions is derived. To form a common basis for the article series, a comprehensive formalism is reviewed and general issues, such as the separation between fixed and variable uncertainties, discussed. As a complement to the theoretical characterization, limited to linear situations, the possibility of using repeated simulations is also described. PACS Nos.: 42.68A, 07.07D, 07.57K

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.865
Threshold uncertainty score0.385

Codex and Gemma teacher scores by category

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

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 head0.056
GPT teacher head0.236
Teacher spread0.180 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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

Citations27
Published2002
Admission routes1
Has abstractyes

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