Research to Improve Oil Spill Response in the Arctic - A Joint Industry Programme
Notice bibliographique
Résumé
Abstract For more than 50 years, the oil and gas industry has funded and conducted research to improve oil spill response technologies and methodologies with industry, government, academia, and stakeholders jointly involved. This research has included hundreds of studies, laboratory and basin experiments and field trials, specifically in the United States, Canada and Scandinavia. Recent examples include the SINTEF Oil in Ice JIP (2006-2009) http://www.sintef.no/Projectweb/JIP-Oil-In-Ice/Publications and research conducted at Ohmsett - The National Oil Spill Response Research and Renewable Energy Test Facility www.ohmsett.com/activities.html. This sustained and frequently collaborative effort is not commonly known and recognized by those outside the field of oil spill response. To build on this existing research and continue improving the technologies and methodologies for arctic oil spill response, ten international oil and gas companies (BP, Chevron, ConocoPhillips, Eni, ExxonMobil, Gazprom-neft, North Caspian Operating Company (NCOC), Shell, Statoil, and Total) are working collaboratively in the Arctic Oil Spill Response Technology - Joint Industry Programme (JIP). The goal is to advance arctic oil spill response strategies and equipment as well as to increase understanding of potential impacts of oil on the marine environment. The programme is coordinated by an Executive Steering Committee comprising representatives from each company. The International Association of Oil and Gas Producers (OGP) is providing project management expertise and the world's foremost experts on oil spill response, development, and operations from across industry, academia, and independent scientific institutions are being engaged to perform the scientific research. The JIP has completed phase one that included technical assessments and state of knowledge reviews in the following six areas: dispersants, environmental effects, trajectory modelling, remote sensing, mechanical recovery, and in situ burning (ISB). Nine research reports are available on the JIP website (www.arcticresponsetechnology.org) that identified and summarised the state-of- knowledge and regulatory status for using dispersants, remote sensing and ISB in the Arctic. Phase two activities are now underway that include laboratory, small and medium scale tank tests, and field research. Eleven projects are in progress ranging from dispersant effectiveness testing; modelling the fate of dispersed oil in ice; assessing the environmental effects of an arctic oil spill; advancing oil spill modelling trajectory capabilities in ice; extending the capability to detect and map oil in darkness, low visibility, in and under ice; improving efficiency of mechanical recovery equipment in ice; chemical herder fate and effects; and expanding the ‘window of opportunity’ for ISB response operations. This paper presents recent JIP progress.
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 | 0,063 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,007 | 0,004 |
| Science ouverte | 0,003 | 0,008 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,003 |
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 ».