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Record W2002718730 · doi:10.5589/q05-003

Assessing the Global Availability and Reliability of the Mars Network, a Proposed Global Navigation Satellite System for Mars

2005· article· en· W2002718730 on OpenAlexaffvenue
Kyle O’Keefe, G. Lachapelle, S. Skone

Bibliographic record

VenueCanadian aeronautics and space journal · 2005
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMars Exploration ProgramComputer scienceConstellationReliability (semiconductor)Mars landingPlanetExploration of MarsSatelliteRange (aeronautics)Remote sensingReal-time computingAerospace engineeringEngineeringGeographyAstrobiologyPhysics

Abstract

fetched live from OpenAlex

In 1999, the NASA Jet Propulsion Laboratory presented a proposal for a six-satellite navigation and communication network for Mars called the Mars Network. In this paper the Mars Network proposal is evaluated in terms of availability, accuracy, and reliability as a function of position and time by simulating network geometry for users distributed across the planet. The Network is found to provide the best service to users in equatorial and polar regions but shows significant deficiencies for mid-latitude users. Instantaneous positioning is limited because of the small number of satellites in the constellation, meaning that users will have to wait for an appropriate geometry to obtain solutions. The lack of redundant observations also means that blunder detection will be difficult and will only be possible for a user making multiple observations over time. The addition of a height constraint to reduce the number of unknowns is shown to increase the range of positions on the planet where instantaneous positioning will be possible; however, instantaneous positioning is available less than one fifth of the time at low latitudes and is still not available at the poles.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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 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

Citations5
Published2005
Admission routes2
Has abstractyes

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