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Record W4396769137 · doi:10.1016/j.icarus.2024.116118

Determining the population of large meteoroids in major meteor showers

2024· article· en· W4396769137 on OpenAlexaff
Karol Wiśniewski, Peter Brown, Danielle E. Moser, Randy Longenbaugh

Bibliographic record

VenueIcarus · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
FundersMarshall Space Flight Center
KeywordsMeteoroidMeteor (satellite)AstronomyPopulationAstrobiologyEnvironmental sciencePhysics

Abstract

fetched live from OpenAlex

We have estimated the largest meteoroids present in major meteor showers from observations conducted between 2019-2022 by the Geostationary Lightning Mapper (GLM) instrument on the GOES-R satellites. Our integrated time area products for the Leonids, Perseids and eta Aquariids are of order 5 × 1010 km2 hours. We compute photometric masses for shower fireballs using the approach of Vojáček et al. (2022) to correct from narrow-band GLM luminosity to bolometric luminosity and apply the luminous efficiency relation of Ceplecha & McCrosky (1976) at high speeds. Between 2019 and 2022, the showers definitely observed by GLM were the Leonids, Perseids, and eta Aquariids, with probable detections of the Orionids and Taurids. We find the largest meteoroids to be of order 7 kg for the Leonids, 3 kg for the Perseids, and 3 kg for the eta Aquariids, corresponding to meteoroids of ≈0.2 m diameter. The Orionids and Taurids had maximum meteoroid masses of 4 kg and 150 kg respectively. The Leonids and eta Aquariids are well fit by a single power-law with differential mass exponent, s, of 2.08 ± 0.08 and 2.00 ± 0.09 over the mass range 10−7 < m < 1 kg. All showers had maximum meteoroid masses compatible with Whipple gas-drag ejection, with the exception of the Perseids which have much larger meteoroids than expected a result also consistent with observations from ground based instruments. This may reflect preferential ejection in narrow jets or possibly some form of mantle erosion/release in the past for the parent comet, 109P/Swift-Tuttle.

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 score0.046
Threshold uncertainty score0.243

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 studies0.0000.000
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 head0.008
GPT teacher head0.256
Teacher spread0.247 · 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

Citations7
Published2024
Admission routes1
Has abstractyes

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