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Record W4362697713 · doi:10.3847/psj/acc4b9

Stereophotoclinometry on the OSIRIS-REx Mission: Mathematics and Methods

2023· article· en· W4362697713 on OpenAlexafffund
R. W. Gaskell, O. S. Barnouin, M. G. Daly, E. E. Palmer, J. R. Weirich, C. M. Ernst, R. T. Daly, D. S. Lauretta

Bibliographic record

VenueThe Planetary Science Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsYork University
FundersCanadian Space AgencyScience Mission Directorate
KeywordsOsirisAsteroidSpacecraftParallaxAstrobiologyPlanetAstronomyPhysicsComputer scienceComputer graphics (images)Biology

Abstract

fetched live from OpenAlex

Abstract Stereophotoclinometry (SPC) makes it possible to extract the shapes of surfaces by combining information from images, namely stereo parallax data and surface shading from slopes, with knowledge of the location of a spacecraft. This technique has been used extensively in the past few decades to describe the shapes of planets and small bodies, such as asteroids and comets. It has also been used to carefully navigate spacecraft around very small bodies, as in the case of the OSIRIS-REx mission to the ∼500 m diameter asteroid (101955) Bennu. This paper describes the mathematical foundation of SPC, with examples from the OSIRIS-REx mission.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.005

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 head0.321
Teacher spread0.280 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations41
Published2023
Admission routes2
Has abstractyes

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