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Record W2045837255 · doi:10.1109/tgrs.2011.2152849

Geolocation of Argus Flight Data

2011· article· en· W2045837255 on OpenAlexafffundabout
Hugh Chesser, Regina Lee, Guy Benari, Rajinder K. Jagpal, Kenneth Lam, Brendan M. Quine

Bibliographic record

VenueIEEE Transactions on Geoscience and Remote Sensing · 2011
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Aeronautics and Space Institute
KeywordsArgusGeolocationComputer scienceRemote sensingFocus (optics)Position (finance)PhysicsGeologyOptics

Abstract

fetched live from OpenAlex

In this paper, we briefly describe the Argus spectrometer and its mission. We then focus on the process to determine the geolocation of the spectrometer's flight data. For the Canadian Advanced Nanospace eXperiment 2 (CanX-2) Argus flight, we have used Simplified General Perturbations 4 (SGP4) propagation for position determination. Two sets of flight data are presented as examples. We estimate the uncertainty in the geolocation of Argus data using this method and investigate potential improvements for future Argus flights.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.041
GPT teacher head0.225
Teacher spread0.184 · 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

Citations9
Published2011
Admission routes3
Has abstractyes

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