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Enregistrement W4324357162 · doi:10.1093/astrogeo/atad016

What a way to AGU

2023· article· en· W4324357162 sur OpenAlexaboutno aff
Cameron Patterson

Notice bibliographique

RevueAstronomy & Geophysics · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueConferences and Exhibitions Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAstrobiologyPhysics

Résumé

récupéré en direct d'OpenAlex

Cameron Patterson gets down to the nitty-gritty of planning his sustainable conference journey from Ottowa to San Francisco San Francisco: the destination This December, I will be taking the train from Ottowa to San Francisco to attend the 2023 AGU Fall Meeting. I want to show that research travel can be more worthwhile than simply jumping on a train – for networking and sustainability, as well as for the life experience. This amazing journey will take me across a Canadian province, a national border, and 11 US states. It isn't a straightforward train journey, but I have always loved the logistical challenge that comes with organising this kind of trip. Seeing where I will be passing through and what I will be able to see along the way just fills me with excitement and anticipation; optimising the route is the first step to getting the most out of a trip, and that's what I'm trying to do now. Ottawa: the point of departure After spending a few days in Ottowa meeting research colleagues and contacts and taking in the sights of this attractive city, the first leg of my trip will take me to Toronto. VIA Rail Canada offer multiple services per day, but with the journey taking around 4–5 hours, I am going to leave in the morning so that I have some time to explore the vibrant and artsy city of Toronto after I arrive. The onward train leaves bright and early the next morning, so I'll have to find a hotel in Toronto. My first Amtrak train, the Maple Leaf, leaves Toronto Union Station just after 8am and hugs Lake Ontario around to Niagara Falls. After crossing the border between Canada and the United States, at the Niagara River, we'll need to disembark to pass through immigration. The train continues its journey towards New York City, but I will be getting off a bit earlier in Buffalo. I have the afternoon to explore before heading back to the station for my overnight train to Chicago. My next train, the Lake Shore Limited, leaves Buffalo Depew Station just after midnight. Under the cover of darkness, we will travel along the banks of Lake Erie through Pennsylvania and into Ohio. The sun will be up as we leave Ohio and make our way through Indiana, before following to the southern tip of Lake Michigan around into Illinois. We arrive in Chicago just after 10am. I will be spending a night here, as the next train journey will be the longest yet and I do not want to risk any delays that would mean that I miss my connection. After finding somewhere quiet to get a bit of work done, I plan to hunt down a slice of the famous Chicago-style pizza. At 2pm the next day, we leave Chicago Union Station aboard the California Zephyr. One of the most scenic train journeys across the United States, travelling almost 4000 kilometres to San Francisco over the next 51 hours. As we make our way into Iowa, we cross the Mississippi River. We travel through the entirety of Nebraska by night; at sunrise we're in Colorado, reversing into Denver just after 7am. I will be staying in Denver, the Mile High City, and the nearby city of Boulder in the foothills of the Rocky Mountains for a few days' work. This is the part of the journey I am most looking forward to; Colorado is famously a breathtakingly beautiful part of the world. We leave Denver Union Station just after 8am, back on board the California Zephyr. This leg of the trip goes all the way to San Francisco. We cross the Rockies, and pass stunning vistas of mountains, canyons and lakes as we dart through Colorado into Utah. We spend the night cruising across Utah, past the Great Salt Lake and into Nevada. The next day we arrive in California and pass by the historical Donner Pass through the stunning Sierra Nevada mountains and down into Emeryville, the station that serves San Francisco, at 5pm. And with that, the journey will be done! Meanwhile, all those travelling by air are fidgeting around in their seats trying to get comfortable, the blanket of white clouds outside their tiny window blocking any glimpse of the wonders below. I, by contrast, will have seen a swathe of continental North America, met colleagues and made new friends, by letting the train take the strain… Cameron Patterson is a third-year PhD student at Lancaster University, UK, working on how space weather affects railway signalling systems. You can find out if this epic, but sustainable, rail journey pans out as planned in future issues of A&G.

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)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,113
Score d'incertitude au seuil0,378

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,012
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0210,006
Communication savante0,0240,024
Science ouverte0,0030,010
Intégrité de la recherche0,0130,016
Charge utile insuffisante (le modèle a refusé de juger)0,1130,072

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 Opus0,026
Tête enseignante GPT0,294
Écart entre enseignants0,268 · 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'étudeSans objet
Domainenon disponible
GenreCommentaire

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
Publié2023
Routes d'admission1
Résumé présentoui

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