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Record W7096311346

Evaluation of the ACE FTS for obtaining nadir measurements

2015· article· en· W7096311346 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicAtmospheric Ozone and Climate
Canadian institutionsnot available
Fundersnot available
KeywordsNadirOccultationTrace gasSatelliteAtmosphere (unit)OzoneCarbon monoxideSpectrometer
DOInot available

Abstract

fetched live from OpenAlex

The SciSat-1 mission is a dedicated Canadian science satellite that will investigate processes that control the distribution of ozone in the stratosphere. The SciSat-1 satellite consists of primarily two science instruments; an Atmospheric Chemistry Experiment (ACE) high-resolution Fourier-transform spectrometer (FTS), and an ultraviolet/visible/near-infrared spectrograph. These instruments will primarily function in occultation mode; however, during the dark portion of the orbit the Earth will pass between the Sun and the satellite. This configuration will give rise to the opportunity of acquiring some nadir-view FTIR spectra of the Earth. Since the ACE FTS was designed to view a hot source (i.e., the Sun) at high resolution using a single scan, it is necessary to determine if the FTS will provide nadir spectra of the relatively cold atmosphere and surface with a sufficient signal-to-noise ratio. Methane, ozone and carbon monoxide gases were used in the cell for the purpose of determining the measurement characteristics of the ACE FTS instrument for a low-intensity source. These measurements were compared with data obtained from the Interferometric Monitor for Greenhouse (IMG) gases onboard the ADEOS satellite. The results show that the ACE FTS should be able to measure the abundant trace gases in the atmosphere with sufficient signal-to-noise ratio.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.241
GPT teacher head0.311
Teacher spread0.070 · 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 designBench or experimental
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

Citations0
Published2015
Admission routes1
Has abstractyes

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