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Record W2241989262

Repetitive Seabed Mapping to Constrain Iceberg Scour Frequency Estimates, Offshore Labrador

2009· article· en· W2241989262 on OpenAlexaboutno aff
G V Sonnichsen, Carrie Breton, Erin Carr, Patrick Campbell, Tony King

Bibliographic record

VenueProceedings of the International Conference on Port and Ocean Engineering Under Arctic Conditions · 2009
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicArctic and Antarctic ice dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSeabedGeologySubmarine pipelineIcebergOceanographySubseaKeelBathymetrySonarSea ice
DOInot available

Abstract

fetched live from OpenAlex

Subsea facilities and pipelines offshore Labrador will require protection from damage from keel-dragging icebergs, however, protection requirements remain uncertain, in large part because the impact frequency is poorly constrained. New Labrador shelf iceberg scour frequency estimates are presented based on repetitive seabed mapping surveys over 18 to 27 years. Two sidescan sonar mosaics were compared over 25 years on eastern Saglek Bank between 156 and 192 metres water depth (mwd). Four new furrows were recognized, resulting in an average scour frequency of 3.8×10⁻³ km⁻² yr⁻¹. On western Saglek Bank between 140 and 175 mwd, 2 new furrows were identified comparing 1981 sidescan sonar to 2006 multibeam sonar data. The calculated scour rate for western Saglek Bank is 2.2×10⁻³ km⁻² yr⁻¹. New ice scour events occurred most frequently at Caroline, the most northern site and also the shallowest in 115 to 121 metres water depth: 12 new furrows and 3 pits occurred between 1979 and 2006: the combined impact rate for both furrows and pits is 4.4×10⁻² km⁻² yr⁻¹. The Saglek Bank repetitive mapping sites provide a valuable upper constraint on scour frequency estimates for Makkovik Bank, the site of proposed natural gas development, approximately 500 km to the south.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.510

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 science0.0000.000
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 head0.016
GPT teacher head0.223
Teacher spread0.207 · 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 designObservational
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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Conference on Port and Ocean Engineering Under Arctic ConditionsSame topicArctic and Antarctic ice dynamicsFrench-language works237,207