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Record W4293147025 · doi:10.3847/psj/ac66eb

Apophis Planetary Defense Campaign

2022· article· en· W4293147025 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueThe Planetary Science Journal · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of Victoria
FundersPlanetary Science DivisionScience Mission DirectorateJet Propulsion LaboratoryAmes Research CenterMinistry of Science and Higher Education of the Russian FederationTürkiye Bilimsel ve Teknolojik Araştırma KurumuQueen's UniversityCalifornia Institute of TechnologyEuropean CommissionMoscow Center of Fundamental and Applied MathematicsNuclear Safety and Security CommissionBrinson FoundationNational Research FoundationSpace Telescope Science InstituteUniversity of ArizonaRussian Academy of SciencesQueen's University BelfastNational Aeronautics and Space Administration
KeywordsAsteroidNear-Earth objectAeronauticsComputer scienceAstrobiologyEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract We describe results of a planetary defense exercise conducted during the close approach to Earth by the near-Earth asteroid (99942) Apophis during 2020 December–2021 March. The planetary defense community has been conducting observational campaigns since 2017 to test the operational readiness of the global planetary defense capabilities. These community-led global exercises were carried out with the support of NASA’s Planetary Defense Coordination Office and the International Asteroid Warning Network. The Apophis campaign is the third in our series of planetary defense exercises. The goal of this campaign was to recover, track, and characterize Apophis as a potential impactor to exercise the planetary defense system including observations, hypothetical risk assessment and risk prediction, and hazard communication. Based on the campaign results, we present lessons learned about our ability to observe and model a potential impactor. Data products derived from astrometric observations were available for inclusion in our risk assessment model almost immediately, allowing real-time updates to the impact probability calculation and possible impact locations. An early NEOWISE diameter measurement provided a significant improvement in the uncertainty on the range of hypothetical impact outcomes. The availability of different characterization methods such as photometry, spectroscopy, and radar provided robustness to our ability to assess the potential impact risk.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.000

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 head0.009
GPT teacher head0.196
Teacher spread0.187 · 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