AUGO II: a comprehensive subauroral zone observatory
Notice bibliographique
Résumé
The annual fall AGU meeting is an interdisciplinary event drawing in a diverse group of people working in earth and space sciences. Our research encompasses space physics, namely electric currents and magnetic fields in space driven by the solar wind and interplanetary magnetic field that cause the aurora borealis. Martin and I attended a ground based magnetometry workshop (Geospace Environment Modeling meeting -- GEM) immediately preceding the AGU meeting. This small, informal gathering brings space physics researchers who operate ground based magnetometer networks (like ourselves) up to date with current and upcoming research projects, some of which Martin may want to participate in. Of particular interest was the International Space Weather Meridian Circle Program, a Chinese initiative to build a global meridian magnetometer chain that will pass through portions of Canada. I presented a poster announcing the impending construction of a new science facility to study the aurora and other related solar-terrestrial phenomena. Most questions directed to my poster dealt with the basic details of our new observatory – where it will be built, when it will be built, will it be built (an important point), and what kinds of instrumentation will it host. Only one criticism was raised that questioned the wisdom of placing an optical observatory in Alberta, given the poor (cloudy) weather we have experienced over the past year. Perhaps more important is the networking aspect of attending such a high-profile conference. It is one of the few opportunities we have to meet with fellow research collaborators. For myself, it allows me to discuss some of the more technical aspects of our research activities, as well as look at new processes and technologies to further advance our research. Specifically, I met with our colleagues at UCLA who designed a web based data repository system (SPASE Tools), obtaining guidance on successfully implementing a web based virtual magnetic observatory (VMO). This is a system to share magnetic data and related metadata with similar SPASE compliant virtual observatories over the Internet. I also discussed with UCLA and others ways of expanding our metadata data set to include additional data not currently described by SPASE. This will be important once we start gathering optical and meteorological data from AUGO. From the earth and space science informatics sessions, I gathered that other research groups (particularly the Japanese IUGONET project) are expanding on the SPASE / VMO concept by developing a richer metadata dictionary (compatible with SPASE) that incorporates more diverse data types. I also had a chance to discuss with colleagues at University of Alberta our recent data sharing arrangement with the Canadian Space Science Data Portal (CSSDP). The education and public outreach sessions showed us some innovative work being done by other groups in putting optical and magnetic auroral data on the mobile web in the form of smartphone web apps. This is something that would be useful for our own observatory for science outreach and teaching, if time and manpower permits. Finally, AGU is one of the few opportunities where we can meet face to face with our Japanese research colleagues who use our data and/or have instruments stationed at AUGO. This is important because the Japanese have been one of the biggest supporters (users) of the AUGO observatory. What I have found given my attendance at two previous AGU meetings is their usefulness as networking events. The poster sessions in particular seem to be the most productive. We meet face to face with our research partners, find answers to questions (in my case, typically technical ones), and learn of new and innovative projects others in the field are doing. My main purpose was to publicize our new auroral observatory, AUGO II. The abstract is now published in the AGU scientific program, so the word is out there. As time goes on, we may find new researchers step forward wanting to know what our new facility has to offer, and hopefully partner with us.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,006 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».