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Record W3097684409 · doi:10.1051/0004-6361/202038649

FRIPON: a worldwide network to track incoming meteoroids

2020· article· en· W3097684409 on OpenAlexaboutno aff

Bibliographic record

VenueAstronomy and Astrophysics · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilColegiul Consultativ pentru Cercetare-Dezvoltare şi InovareCentre National d’Etudes SpatialesObservatoire de Paris, Université de Recherche Paris Sciences et LettresSorbonne UniversitéAgence Nationale de la RechercheMuséum National d'Histoire NaturelleUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiCentre National de la Recherche ScientifiqueMinistério da Ciência, Tecnologia, Inovações e ComunicaçõesIstituto Nazionale di AstrofisicaConseil Régional, Île-de-France
KeywordsMeteoroidMeteoriteInterplanetary spaceflightEvent (particle physics)Interplanetary mediumTrack (disk drive)Interplanetary dust cloud

Abstract

fetched live from OpenAlex

Context.Until recently, camera networks designed for monitoring fireballs worldwide were not fully automated, implying that in case of a meteorite fall, the recovery campaign was rarely immediate. This was an important limiting factor as the most fragile – hence precious – meteorites must be recovered rapidly to avoid their alteration. Aims.The Fireball Recovery and InterPlanetary Observation Network (FRIPON) scientific project was designed to overcome this limitation. This network comprises a fully automated camera and radio network deployed over a significant fraction of western Europe and a small fraction of Canada. As of today, it consists of 150 cameras and 25 European radio receivers and covers an area of about 1.5 × 106km2. Methods.The FRIPON network, fully operational since 2018, has been monitoring meteoroid entries since 2016, thereby allowing the characterization of their dynamical and physical properties. In addition, the level of automation of the network makes it possible to trigger a meteorite recovery campaign only a few hours after it reaches the surface of the Earth. Recovery campaigns are only organized for meteorites with final masses estimated of at least 500 g, which is about one event per year in France. No recovery campaign is organized in the case of smaller final masses on the order of 50 to 100 g, which happens about three times a year; instead, the information is delivered to the local media so that it can reach the inhabitants living in the vicinity of the fall. Results.Nearly 4000 meteoroids have been detected so far and characterized by FRIPON. The distribution of their orbits appears to be bimodal, with a cometary population and a main belt population. Sporadic meteors amount to about 55% of all meteors. A first estimate of the absolute meteoroid flux (mag < –5; meteoroid size ≥~1 cm) amounts to 1250/yr/106km2. This value is compatible with previous estimates. Finally, the first meteorite was recovered in Italy (Cavezzo, January 2020) thanks to the PRISMA network, a component of the FRIPON science project.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.009
GPT teacher head0.197
Teacher spread0.188 · 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

Citations116
Published2020
Admission routes1
Has abstractyes

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