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Small near-Earth Objects in the Taurid Resonant Swarm

2025· article· en· W4415080796 on OpenAlexafffund
Quanzhi Ye, Jasmine Li, Denis Vida, David L. Clark, Eric C. Bellm

Bibliographic record

VenueActa Astronautica · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaNational Aeronautics and Space Administration
KeywordsSwarm behaviourAsteroidPopulationTransient (computer programming)Resonance (particle physics)

Abstract

fetched live from OpenAlex

The Taurid Resonant Swarm (TRS) within the Taurid Complex hosts dynamically-concentrated debris in a 7:2 mean-motion resonance with Jupiter. Fireball observations have confirmed that the TRS is rich in sub-meter-sized particles, but whether this enhancement extends to larger, asteroid-sized objects remains unclear. Here we reanalyze the data obtained by a Zwicky Transient Facility (ZTF) campaign during the 2022 TRS encounter, and find that the TRS may host up to ∼ 1 0 2 Tunguska-sized objects and up to ∼ 1 0 3 Chelyabinsk-sized objects, the latter of which agrees the estimate derived from bolide records. This translates to an impact frequency of less than once every 4 million years. However, we caution that these numbers are based on the unverified assumption that the orbital distribution of the TRS asteroids follows that of fireball-sized meteoroids. Future wide-field facilities, such as the Vera C. Rubin Observatory, could take advantage of TRS’s close approaches in the 2020–30s and validate the constraints of the asteroid-sized objects in the TRS.

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 score0.008
Threshold uncertainty score0.017

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 communication0.0010.001
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 head0.011
GPT teacher head0.221
Teacher spread0.210 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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