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Record W2536388523 · doi:10.1002/asna.201613273

V‐type near‐Earth asteroids: Dynamics, close encounters and impacts with terrestrial planets

2017· article· en· W2536388523 on OpenAlexaffabout
Mattia Galiazzo, Elizabeth A. Silber, David Bancelin

Bibliographic record

VenueAstronomische Nachrichten · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsImpact craterAsteroidTerrestrial planetPlanetAstrobiologyGeologyMars Exploration ProgramVenusAsteroid beltSolar SystemEarth (classical element)AstronomyPhysics

Abstract

fetched live from OpenAlex

Background Asteroids colliding with planets vary in composition and taxonomical type. Among Near‐Earth Asteroids (NEAs) are the V‐types, basaltic asteroids that are classified via spectroscopic observations. Materials and Methods we performed numerical simulations and statistical analysis of close encounters and impacts between V‐type NEAs and the terrestrial planets over the next 10 Myr. We study the probability of V‐ type NEAs colliding with Earth, Mars and Venus, as well as the Moon. We perform a correlational analysis of possible craters produced by V‐type NEAs, using available catalogs for terrestrial impact craters. Results The results suggest that V‐type NEAs can have many close encounters below 1 LD and even some of them impacts with all the terrestrial planets, the Earth in particular. There are four candidate craters on Earth that were likely caused by V‐type NEAs. Conclusion At least 70% of the V‐type NEAs can have close encounters with the terrestrial planets (and 58% with the Moon) . They can collide with all the terrestrial planets. In particular for the Earth the average rate is one every ∼12 Myr. The two craters with the highest probability of being generated by an impact with a basaltic impactor are: the Strangways crater (24 km diameter) in Australia and the Nicholson crater (12.5 km diameter) in Canada.

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)0.000
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 score0.017
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.007
GPT teacher head0.219
Teacher spread0.212 · 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

Citations4
Published2017
Admission routes2
Has abstractyes

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