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Record W2598579506 · doi:10.1111/maps.12854

Possible potentially threatening co‐orbiting material of asteroid 2000EE104 identified through interplanetary magnetic field disturbances

2017· article· en· W2598579506 on OpenAlexaff
Hairong Lai, C. T. Russell, H. Y. Wei, Martin Connors, Gian Luca Delzanno

Bibliographic record

VenueMeteoritics and Planetary Science · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsAthabasca University
FundersNational Aeronautics and Space Administration
KeywordsAsteroidAstrobiologySpacecraftPhysicsInterplanetary spaceflightAstronomyOrbit (dynamics)Solar SystemInterplanetary dust cloudGeophysicsAerospace engineeringSolar windMagnetic field

Abstract

fetched live from OpenAlex

Abstract Near‐Earth objects (NEOs) with diameters of <300 m are difficult to detect from the Earth with radar or optical telescopes unless and until they approach closely. If they are on collisional courses with the Earth, there is little that can be done to mitigate the considerable damage. Although destructive collisions in space are rare for 1 km diameter bodies and above, once hit by a sizeable impactor, such a NEO can develop a relatively dense cloud of co‐orbiting material in which destructive collisions are relatively frequent. The gas and nanoscale dust released in the destructive collisions can be detected remotely by downstream spacecraft equipped with magnetometers. In this paper, we use such magnetic disturbances to identify regions of near‐Earth space in which high densities of small objects are present. We find that asteroid (138175) 2000EE104 currently may have a cloud of potentially threatening co‐orbiting material. Due to the scattered co‐orbitals, there can be a finite impact probability whenever the Earth approaches the orbit of asteroid 2000EE104, regardless of the position of the asteroid itself.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.260
Teacher spread0.248 · 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 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

Citations8
Published2017
Admission routes1
Has abstractyes

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