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Record W2558721361 · doi:10.4043/27472-ms

An Iceberg Drift Prediction Study Offshore Newfoundland

2016· article· en· W2558721361 on OpenAlexfundaboutno aff
Leif Erik Andersson, Lars Imsland, Francesco Scibilia

Bibliographic record

VenueArctic Technology Conference · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
FundersNational Research Council CanadaArcticNet
KeywordsIcebergSubmarine pipelineTrajectoryCurrent (fluid)Scheme (mathematics)EstimatorProcess (computing)MeteorologyComputer scienceGeodesyGeologyGeographyStatisticsMathematicsOceanographySea icePhysics

Abstract

fetched live from OpenAlex

Abstract Iceberg drift forecast is a challenging process. Large uncertainties in iceberg geometry and in the driving forces – current, wind and waves – make accurate forecasts difficult. The two forecast schemes, the ancillary current and the inertial current estimation-forecast scheme are presented. In both schemes, the moving horizon estimator is used, to estimate the needed parameters. Furthermore, a switching scheme is proposed, which switches between the two iceberg drift forecast schemes. A criterion is introduced that identifies when to switch between both schemes. The switching scheme is implemented and tested on an iceberg drift trajectory, measured during a research expedition offshore Newfoundland conducted by ArcticNet and Statoil. It is shown, that the use of two forecast schemes and a timely decision which scheme to use, improves the iceberg drift forecast compared to using only one scheme.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.605

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.012
GPT teacher head0.222
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations1
Published2016
Admission routes2
Has abstractyes

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