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Enregistrement W4400289640 · doi:10.5194/epsc2024-928

Update on the FRIPON network and the effectiveness of meteorite recovery in Europe including all fireball networks. The example of the Saint Pierre-le-Viger fall highlights the need for Pro-Am collaboration

2024· preprint· en· W4400289640 sur OpenAlexaffabout
F. Colas, B. Zanda, Pierre Vernazza, Adrien Malgoyre, Sylvain Bouley, Lucie Maquet, Asma Steinhausser, Auriane Egal, J. Gattacceca, Mirel Birlan, K. Antier, S. Bouquillon, Daniele gardio, Björn Poppe, Jim Rowe, A. J. King, H. Lamy, Salma Sylla, Sebastiaan De Vet, Z. Benkhaldoun

Notice bibliographique

Revuenon disponible
Typepreprint
Langueen
DomainePhysics and Astronomy
ThématiqueAstro and Planetary Science
Établissements canadiensEspace pour la vie
Organismes subventionnairesnon disponible
Mots-clésMeteoriteSAINTFall of manAstrobiologyHistoryArt historyPolitical sciencePhysicsLaw

Résumé

récupéré en direct d'OpenAlex

FRIPON projectThe FRIPON fireball project was initially conceived and founded in France in 2014 with a grant from the ANR (Agence Nationale de la Recherche), the objective was to cover the country with a dense network of all-sky cameras (~ a hundred with 80 km spacing). We built (Colas at al 2020) [1] a centralized network, a data storage architecture and a real-time data processing (astrometry of each camera, triangulation of each event to calculate the trajectory of the bright flight and finally determination of the scientific parameters: orbit, incoming mass, final mass, etc.). A catalog of orbits is produced each year and is available on the fireball.fripon.org website. The FRIPON project is designed as a real-time network, the aim of which is to trigger a search in the field within 24 hours of the fall in order to recover fresh meteorites. Extension and resultsThe architecture developed for the network allows for easy expansion, and from 2016, scientists from neighboring countries were interested in joining the project using the same hardware, software and infrastructure. The main extensions involved Italy (PRISMA), Germany (FRIPON-Germany), Romania (MOROI), the United Kingdom (SCAMP), Canada (DOME), the Netherlands (DOERAK), Spain (SPMN), Belgium (FRIPON-Belgium), Switzerland (FRIPON-Switzerland), South America (FRIPON-Andino), Morocco (MOFID) and Senegal (ASAMAAN). FRIPON (www.fripon.org) is now an international project and the French network is now FRIPON-VigieCiel (www.vigie-ciel.org) a merger of the camera network and of the Vigie-Ciel citizen science project supported by Muséum national d'Histoire naturelle with the aim of involving the general public in finding meteorites by learning how to identify them and thus take part in research. Ten years after the start of the network, we now have 250 active cameras, we have obtained more than 10,000 orbits and our data has been used in the recovery of 7 meteorites (Cavezzo 2020, Winchcombe 2020, Kindberg 2021, Saint-Pierre-le-Viger 2023, Matera 2023, Menetréol 2023, Ribbeck 2024). It is important to note that over these 10 years, more than 20 searches have been organized without positive results, as the recovery efficiency is often far from 100% due to vegetation, private land, etc.Recovery statisticsRoughly 600 detections per year included at least one French camera, as described in (Colas et al 2020) [1] this corresponds to objects larger than 1 cm and is compatible with the surface area of the national territory (10⁶ km²) according to the previous estimate (Brown et al 2002) [2]. As it also predicts the fall of around 10 meteorites per year for France, we hoped at the start of the project to recover about one meteorite per year, which seems realistic: 50% of meteorites fall during the day, cloud cover is around 50% and ground searches are difficult one time out of two. Another clue is that in the 19th century, one meteorite was recovered every two years in France (Colas, 2020) [1]. Unfortunately, after 10 years of operation, we have only recovered 2 meteorites in France, which is a little disappointing but still better than the 20th century efficiency of one meteorite every 10 years. In the end, the realistic recovery rate seems close to one meteorite per year, but for all of Europe! Since the start of the program in 2015, we found 40 events with a final mass greater than 100g and 10 for 500g and more. These data are compatible with our initial estimate, but the recovery success is low due partly to agricultural changes from small farms where owners could easily identify "strange" stones to big intensive farms.The case of 2023 CX1Asteroid 2023 CX1 was discovered by Krisztián Sárneczky of the Konkoly Observatory on 12 February 2023, just 7 hours before it was due to hit the Earth, which made it possible to track it and calculate its orbit very precisely. Most of the telescopic data was obtained by amateurs. It is important to point out that we had to use data from different networks (FRIPON, GMN, AllSky 7, UKMON) and security cameras to calculate the atmospheric entry parameters. The potential strewnfield was then determined in parallel by several groups. The FRIPON/Vigie-ciel collaboration quickly mobilized its network and set up a field search. 4 stones were thus found by children, 4 others by amateurs and finally only 4 others by scientists who were not even meteorite specialists! In the end, amateurs played a fundamental role in the recovery success at all stages of the event: telescopic and fireball data as well as field searches.ConclusionThe 7 meteorites found in Europe in the last 5 years would not have been found without the presence of fireball networks to give the alert and calculate the strewnfields. In Europe, we are fortunate to have a number of networks (FRIPON, GMN, AllSky7, DFN, etc.) that enable us to detect these events exhaustively. The success of our research is also largely due to citizen science programs such as Vigie-Ciel, which make it possible to organize effective field searches. References: [1] Colas et al. (2020) Astronomy and Astrophysics 644, A53; [2] Brown et al (2002) Nature, Volume 420, Issue 6913

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,028
Score d'incertitude au seuil0,093

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,007
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,002
Études des sciences et des technologies0,0010,000
Communication savante0,0030,003
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0280,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,017
Tête enseignante GPT0,234
Écart entre enseignants0,217 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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