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Record W4301116737 · doi:10.5957/icetech-2014-107

Forecasting Pack Ice Blockage Events at St John's Harbour, Newfoundland: An Empirical Approach and Development of Ship Alert and Advisory Tables

2014· article· en· W4301116737 on OpenAlexaffabout
I. M. Turnbull, Richard McKenna, Rocky Taylor, Robert Sarracino

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsHarbourSubmarine pipelineOcean gyreOceanographyIcebergCurrent (fluid)MeteorologyMarine engineeringSea iceEnvironmental scienceGeologyEngineeringGeographyFisheryComputer science

Abstract

fetched live from OpenAlex

Supply vessels for the Grand Banks oil and gas industry have faced 25 ice blockage events entering St. John’s Harbour, Newfoundland from 1979-2005. We propose an empirical approach to forecasting the occurrence of 8-10/10ths total ice concentration at the harbour, and tables for alerting and advising ship personnel on the likelihood of such an event based on offshore winds, surface ocean currents, and ice conditions. These events are primarily controlled by the strength and character of an ocean current gyre and the ice conditions over a large area offshore St. John’s Harbour.

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)0.002
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.627

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.037
GPT teacher head0.235
Teacher spread0.198 · 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

Citations2
Published2014
Admission routes2
Has abstractyes

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