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Record W4396914018 · doi:10.3847/1538-3881/ad496e

A Reference Meteor Magnitude for Intercomparable Fluxes

2024· article· en· W4396914018 on OpenAlexafffund
Althea V. Moorhead, Denis Vida, Peter Brown, M. Campbell‐Brown

Bibliographic record

VenueThe Astronomical Journal · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaMarshall Space Flight CenterNuclear Safety and Security CommissionNational Aeronautics and Space Administration
KeywordsMagnitude (astronomy)Meteor (satellite)Environmental scienceGeologyGeodesyMeteorologyAtmospheric sciencesGeographyPhysicsAstronomy

Abstract

fetched live from OpenAlex

Abstract The rate at which meteors pass through Earth’s atmosphere has been measured or estimated many times over; existing flux measurements span at least 12 astronomical magnitudes, or roughly five decades in mass. Unfortunately, the common practice of scaling flux to a universal reference magnitude of +6.5 tends to collapse the magnitude or mass dimension. Furthermore, results from different observation networks can appear discrepant due solely to the use of different assumed population indices, and readers cannot resolve this discrepancy without access to magnitude data. We present an alternate choice of reference magnitude that is representative of the observed meteors and minimizes the dependence of flux on population index. We apply this choice to measurements of recent Orionid meteor shower fluxes to illustrate its usefulness for synthesizing independent flux measurements.

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)0.006
metaresearch head score (Gemma)0.029
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 score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.029
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0090.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.003

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 head0.024
GPT teacher head0.275
Teacher spread0.251 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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