Experiences and Lessons Learned in the Engineering Design and Construction in the Alaska Arctic
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Notice bibliographique
Résumé
The Alaska Arctic is located at north of the Brooks Range and from the Bering Sea to the Canadian border, with an arctic marine climate. Cold and continuous permafrost with thicknesses from 200 to 300 m, sometimes to 700 m, are widespread. The most prominent surface manifestations of the underlying permafrost include numerous small lakes and ponds, ice-wedge polygons and tundra wetlands on the arctic coastal plain. The engineering construction in the Alaska Arctic was mainly driven by naval and commercial exploration, development and transportation of crude oil and natural gas from the Prudhoe Bay, Cape Simpson, Umiat and Barrow areas, and some military operations, such as the Distant Early Warning Line radar stations since 1940s. There are many experiences, lessons learned and body of knowledge obtained during all these engineering construction periods. The most successful engineering feats include the exploration and later development of the Prudhoe Bay oil/gas field, Alyeska Hot Oil Pipeline, and environmental protection regulations during most of these engineering activities, which resulted only minor impacts considering so many mega-projects were undertaken with very limited knowledge of permafrost terrain in advance. In order to successfully and economically engineer for construction and operations in the arctic, it is necessary to think cold, and to plan and act accordingly. The construction engineer must be innovative and not be bound by mid-latitude mind-settings gained from education, training or conventional wisdom. The engineer and the environmental scientist must work as a team during the initial field survey, during the design phase, and during the actual field construction. The engineer needs to know the environmental parameters, constraints and potential opportunities. The environmental scientist needs to know the engineer's construction design and problems, and understand the engineering constraints, equipment capabilities, and the economics of potential alternative courses of action. These understandings cannot be acquired working alone, then trying to coordinate results after each has invested time and effort and developed plans and positions which they are reluctant to modify.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
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 découle