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Record W2885390363 · doi:10.3847/2041-8213/aada46

In Search of Recent Disruption of (3200) Phaethon: Model Implication and <i>Hubble Space Telescope</i> Search

2018· article· en· W2885390363 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Letters · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteor (satellite)AsteroidSpacecraftDozenOrbit (dynamics)MeteoroidMeteor showerLimit (mathematics)

Abstract

fetched live from OpenAlex

Abstract Near-Earth asteroid (3200) Phaethon is notable for its association with a strong annual meteor shower, the Geminids, indicative of one or more episodes of mass ejection in the past. The mechanism of Phaethon’s past activity is not yet understood. Here, we present a Hubble Space Telescope ( HST ) search of meter-sized fragments in the vicinity of Phaethon, carried out during Phaethon’s historic approach to Earth in mid-December of 2017. Numerical simulations conducted to guide HST ’s pointing also show that the dynamical evolution of Phaethon-originated particles is quick, as ejected materials take no longer than ∼250 years to spread to the entire orbit of Phaethon. Our search was completed down to a 4 m class limit (assuming Phaethon-like albedo) and was expected to detect 0.035% of particles ejected by Phaethon in the past several decades. The negative result of our search capped the total mass loss of Phaethon over the past few dozen orbits to be 10 12 kg at the 3 σ level, taking the best estimates of size power-law from meteor observations and spacecraft data. Our result also implies a millimeter-sized dust flux of within 0.1 au of Phaethon, suggesting that any Phaethon-bound mission is unlikely to encounter dense dust clouds.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.067
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.019
GPT teacher head0.277
Teacher spread0.258 · 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 designSimulation or modeling
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

Citations23
Published2018
Admission routes1
Has abstractyes

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