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It's a Dirty Job But No One Has to Do It: Collecting Geophysical Data in the Arctic Ocean

2023· article· en· W7029400977 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of New Hampshire Scholars Repository (University of New Hampshire at Manchester) · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueCross-Border Cooperation and Integration
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésUnconformityRidgeArcticStructural basinSeafloor spreadingGlacial periodCanada BasinOceanic basin
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The history of the Arctic Ocean is largely unwritten, but can be glimpsed through the fragments of what we know. My career has been defined by finding more fragments to build a panorama of how this ocean basin was created and modified over the last 150 million years. In the Summer of 2021 I boarded the R/V Sikuliaq, UAF’s research vessel, to voyage to the central Arctic Ocean and explore the seafloor and sediments beneath it through the use of sound at various frequencies. Multichannel-seismic data were collected in August and September 2021 over the Northern Chukchi Borderland and Central Canada Basin from the R/V Sikuliaq. The data were acquired with two 520 cu inch GI airguns and a 200 meters (32 channels) streamer. These data were collected to image the stratigraphy on the Borderland and in the Basin to study the evolution of these features. The processed multichannel-seismic profiles from the Northern Chukchi Borderland show horsts with grabens continuous with those imaged from R/V Langseth in 2011. These basins are filled with syn-rift and post-rift stratigraphy. Stratigraphic sequences imaged on Northwind Ridge are segmented by multiple unconformities and minor structures. The origin of these unconformities may be related to the opening of Canada Basin and multiple generations of glacial ice contact over the bathymetric high. The seismic profile on Canada basin showed a prominent feature recognized as a basement, which seems to support the interpretation of the extinct mid-ocean ridge as an unsegmented, ultra-slow spreading ridge. To make this cruise happen, it was necessary to work around a variety of COVID-related obstacles. It was something of a miracle that we left the pier at all. Once we were in the North, we encountered heavy ice conditions that dictated continuous revision to our science plan. We managed to collect good data, which will define some of the ocean’s unknown history. In this lecture, I will present the basics of the history of the Arctic Ocean, how we were able to work there in summer of 2021 and some preliminary results. Presenter Bio Bernard Coakley was born in Detroit, Michigan and attended two of that state’s finer Universities, eventually earning a degree in Geology from the University of Michigan. He attended Louisiana State, where he earned an MS degree and continued on to Columbia University where he received an MPhil and PhD. He had a post-doc at the University of Wisconsin - Madison and returned to Lamont-Doherty as a soft money research scientist. During this time he began to work in the Arctic Ocean, which has become his almost exclusive obsession since 1993. After five years on soft money, he went to Tulane University as an Assistant Professor. For the last twenty-one years he has been at the University of Alaska Fairbanks. To better understand the Arctic Ocean, he has sailed on US Navy fast attack submarine (SCICEX 1993, 1995 and 1999), on icebreakers (USCGC Healy, R/V Sikuliaq and Polarstern) and on relatively unreinforced vessels (R/V Langseth). He has been to the North Pole six times, though he wonders why that geographic singularity excites anyone.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,344
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,072
Tête enseignante GPT0,297
Écart entre enseignants0,225 · 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 tête enseignante, pas un consensus.

Devis d'étudeSans objet
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
Publié2023
Routes d'admission1
Résumé présentoui

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