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Record W2484362170 · doi:10.1093/mnras/stw1846

Dormant comets among the near-Earth object population: a meteor-based survey

2016· article· en· W2484362170 on OpenAlexaffabout
Quanzhi Ye, Peter Brown, Petr Pokorný

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
Fundersnot available
KeywordsMeteoroidAsteroidCometPhysicsAstrobiologyNear-Earth objectPopulationMeteor (satellite)AstronomyPlanetSolar SystemAstrophysics

Abstract

fetched live from OpenAlex

Dormant comets in the near-Earth object (NEO) population are thought to be involved in the terrestrial accretion of water and organic materials. Identification of dormant comets is difficult as they are observationally indistinguishable from their asteroidal counterparts, however, they may have produced dust during their final active stages which potentially are detectable today as weak meteor showers at the Earth. Here we present the result of a reconnaissance survey looking for dormant comets using 13 567 542 meteor orbits measured by the Canadian Meteor Orbit Radar (CMOR). We simulate the dynamical evolution of the hypothetical meteoroid streams originated from 407 near-Earth asteroids in cometary orbits that resemble orbital characteristics of Jupiter-family comets (JFCs). Out of the 44 hypothetical showers that are predicted to be detectable by CMOR, we identify five positive detections that are statistically unlikely to be chance associations, including three previously known associations. This translates to a lower limit to the dormant comet fraction of 2.0 ± 1.7 per cent in the NEO population and a dormancy rate of ∼10−5 yr−1 per comet. The low dormancy rate confirms disruption and dynamical removal as the dominant end state for near-Earth JFCs. We also predict the existence of a significant number of meteoroid streams whose parents have already been disrupted or dynamically removed.

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)0.001
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 score0.026
Threshold uncertainty score0.879

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.010
GPT teacher head0.200
Teacher spread0.190 · 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

Citations35
Published2016
Admission routes2
Has abstractyes

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