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Record W4311121571 · doi:10.3847/psj/aca011

Building a High-resolution Digital Terrain Model of Bennu from Laser Altimetry Data

2022· article· en· W4311121571 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Planetary Science Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsYork University
Fundersnot available
KeywordsRemote sensingTerrainAltimeterAsteroidData setComputer scienceDigital elevation modelSet (abstract data type)FidelityArtificial intelligenceGeologyGeographyCartographyAstrobiologyPhysics

Abstract

fetched live from OpenAlex

Abstract The Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) spacecraft orbited the near-Earth asteroid (101955) Bennu to characterize the asteroid prior to sampling. One important aspect of this characterization was the creation of a high-resolution (5–7 cm) global shape model using the OSIRIS-REx Laser Altimeter (OLA). We describe the data collected by OLA, along with the approach used to register overlapping topography using keypoints and keypoint descriptors in order to produce a globally self-consistent set of data. These globally registered sets of topographic scans were used to generate digital terrain models at both global and regional scales. We also describe efforts to correct for a change in behavior of the scanning mirror after the launch and highlight the improvements to the data after implementing an updated calibration of the mirror. The resulting model represents the highest-fidelity global OLA data set.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.793

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 communication0.0000.002
Open science0.0020.000
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 head0.031
GPT teacher head0.238
Teacher spread0.207 · 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