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Record W3157799528 · doi:10.1364/ao.419249

Adaptation of the polarimetric multi-spectral Aerosol Limb Imager for high altitude aircraft and satellite observations

2021· article· en· W3157799528 on OpenAlexafffund
M. N. Kozun, Adam Bourassa, D. A. Degenstein, C. S. Haley, Sheng Zheng

Bibliographic record

VenueApplied Optics · 2021
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical and Acousto-Optic Technologies
Canadian institutionsHoneywell (Canada)University of Saskatchewan
FundersCanadian Space Agency
KeywordsRemote sensingRadianceStratospherePolarimetryRadiometerSatelliteAerosolEnvironmental scienceTroposphereBreadboardSun-synchronous orbitOpticsGeologyMeteorologyPhysics

Abstract

fetched live from OpenAlex

An elegant breadboard prototype of the Aerosol Limb Imager (ALI) has been developed to meet key performance parameters that will meet requirements for the retrieval of aerosol from the upper troposphere and stratosphere from limb scattered sunlight radiance measurements. Similar in concept to previous high altitude balloon-based generations, this instrument pairs a liquid crystal polarization rotator with an acousto-optic tunable filter to capture polarimetric multi-spectral images of the atmospheric limb. This design improves the vertical resolution, signal-to-noise ratio, and athermalization, all of which will facilitate observation from a moving high altitude aircraft platform, which provides a platform analogous to the spatially varying measurements that would be made from a satellite. Finally, a preliminary design is presented for a satellite-based generation of ALI.

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)0.001
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.025
GPT teacher head0.234
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 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

Citations4
Published2021
Admission routes2
Has abstractyes

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