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Record W6931860417 · doi:10.5281/zenodo.7541028

Review of air-ice-ocean processes in the Margial Ice Zone of importance for offshore activities in the Barents Sea region

2007· report· en· W6931860417 on OpenAlexaboutno aff

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2007
Typereport
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsnot available
Fundersnot available
KeywordsSea iceSubmarine pipelineArctic ice packArcticDrift iceAntarctic sea iceArctic sea ice declineCircumpolar star

Abstract

fetched live from OpenAlex

In this report we have reviewed air-ice-ocean processes in the Marginal Ice Zone (MIZ) of importance for offshore operations and related environmental issues.<br> The MIZ is a characteristic feature of the circumpolar Arctic and sub-Arctic seas, i.e., the European, Russian, US and<br> Canadian parts of the Arctic. The focus has been on the Barents Sea and adjacent regions where offshore exploration plans are quite extensive. The Barents Sea is partly ice- covered in the winter season in the northern and eastern regions. The sea ice in combination with wind, waves and currents provide harsh environmental conditions, especially in the winter. The MIZ can be defined as the zone extending from typical 100 km outside the ice edge to 100 km inside the ice edge, where certain air-ice-ocean processes dominate and have significant impact in the environment. The physical environment is determined by an integrated system of atmospheric, oceanic and sea ice processes, including wind, waves, ocean eddies, jets and current features, convergence/divergences, sea ice processes and their variability. Many of the processes are not well understood, because observations and modeling capability is not yet well developed. The uncertainty in description of many physical processes is a major reason for the large discrepancy between different climate model simulations in the Arctic.<br> It is described first the general environmental and climate processes of the Arctic Ocean, providing and overview of scientific issues to be considered by offshore operators. The main atmospheric processes are reviewed regarding climate as well as meteorological conditions for operations in Arctic and sub-Arctic seas. The sea ice conditions are of major importance because the MIZ is defined by the extent and variability of the ice edge region. Sea ice is also the main constraint for offshore operations and transportation in the Arctic, and improved monitoring and forecasting of sea ice is therefore a major task to ensure safe and cost-efficient operations. The<br> sea ice extent, drift and thickness are determined by dynamic and thermodynamic forcing from the atmosphere and the ocean. A warming of the ocean in the Arctic regions has been observed in recent years, contributing to reduced extent and thickness of sea ice observed in the last two – three decades. In the last few years, the Barents Sea has had less ice in the winter than the average, and this can be attributed to higher ocean temperature as well as to warmer air masses in the region. Sea level change in combination with storm surges and waves will have impact on coastal constructions, vessels and offshore operations. More storms and extreme sea level height and wave height can be expected in the future. In the Barents Sea area, icebergs originating from calving glaciers in Svalbard, Franz Josef Land and Novaya Zemlya represent one of the main hazard factors for offshore operations. The amount of icebergs drifting into the drilling areas varies considerable from year to year. An extreme event of many icebergs drifting into the Shtokman area was observed in May 2003. Prediction of iceberg occurrence in regions of offshore operations is<br> not feasible. It is therefore important to develop good monitoring and forecasting systems for icebergs. The report describes the main elements of the oil spill problem in the MIZ, including observation and modeling of oil spills as well as recovery solutions. Finally, some elements of primary production in the MIZ is described.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.657

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.278
Teacher spread0.226 · 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 designNot applicable
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
Published2007
Admission routes1
Has abstractyes

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