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Record W2625943474

Kinetic Damage from Meteorites

2017· article· en· W2625943474 on OpenAlexaff
William J. Cooke, Peter Brown, Mark Matney

Bibliographic record

VenueNASA STI Repository (National Aeronautics and Space Administration) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteoriteMeteoroidAstrobiologyImpact craterFlux (metallurgy)Cosmic rayGeologyAtmospheric entryAtmosphere (unit)PhysicsAstrophysicsAstronomyMaterials scienceMeteorology
DOInot available

Abstract

fetched live from OpenAlex

A Near Earth object impacting into Earth's atmosphere may produce damaging effects at the surface due to airblast, thermal pulse, or kinetic impact in the form of meteorites. At large sizes (>many tens of meters), the damage is amplified by the hypersonic impact of these large projectiles moving with cosmic velocity, leaving explosively produced craters. However, much more common is simple "kinetic" damage caused by the impact of smaller meteorites moving at terminal speeds. As of this date a handful of instances are definitively known of people or structures being directly hit and/or damaged by the kinetic impact of meteorites. Meteorites known to have struck humans include the Sylacauga, Alabama fall (1954) and the Mbale meteorite fall (1992). Much more common is kinetic meteorite damage to cars, buildings, and even a post box (Claxton, Georgia - 1984). Historical accounts indicate that direct kinetic damage by meteorites may be more common than recent accounts suggest (Yau et al., 1994). In this talk we will examine the contemporary meteorite flux and estimate the frequency of kinetic damage to various structures, as well as how the meteorite flux might affect the rate of human casualties. This will update an earlier study by Halliday et al (1985), adding variations expected in meteorite flux with latitude (Le Feuvre and Wieczorek, 2008) and validating these model predictions of speed and entry angle with observations from the NASA and SOMN fireball networks. In particular, we explore the physical characteristics of bright meteors which may be used as a diagnostic for estimating which fireballs produce meteorites and hence how early warning of such kinetic damage may be estimated in advance through observations and modelling.

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 score0.195
Threshold uncertainty score0.702

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 communication0.0000.000
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 head0.014
GPT teacher head0.255
Teacher spread0.241 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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