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Record W2081234144 · doi:10.4043/24662-ms

Requirements on Logistics to Tow Large Gravity-Based Structures to the Chukchi and Beaufort Sea

2014· article· en· W2081234144 on OpenAlexaff
Markus Wernli, Ken L. FitzGerald, Kåre Hjorteset, Michael W. LaNier

Bibliographic record

VenueOTC Arctic Technology Conference · 2014
Typearticle
Languageen
FieldEngineering
TopicOffshore Engineering and Technologies
Canadian institutionsBerger (Canada)
Fundersnot available
KeywordsSubmarine pipelineScheduleArcticEngineeringMarine engineeringComputer scienceGeologyOceanography

Abstract

fetched live from OpenAlex

Abstract Oil companies are considering large Gravity-Based Structures (GBS) as a potential solution to support operations in new offshore oil and gas field developments in the Arctic seas that are designed for year-round production in potentially 100-meter-deep waters. These structures support multibillion-dollar drilling, production, and storage facilities that are typically commissioned with topsides and consumables before entering the Arctic, and are towed with these elements already installed on the GBS to the installation site. However, the tow not only presents a high risk during the installation phase of the platform, but also has a significant influence on the design and cost of the GBS. Movement of these large offshore structures through the Bering Straits and into position at the installation location includes unique risks that result from the minimal precedence of these tows and the scarcity of local support and contingency ports. This situation suggest that typical approaches to risk identification, design criteria development, and application of risk mitigation approaches may merit reevaluation. This paper will discuss the logistics related to the movement of these large structures and discuss the various cost- and schedule-related risks that have to be identified to support the early design phases of the GBS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.550
Threshold uncertainty score0.760

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.231
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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