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Record W2595718576 · doi:10.3997/2214-4609.201601181

New Insights into the Slope and Deep Water Regions of the Southern Grand Banks Area, Offshore Newfoundland, Canada

2016· article· en· W2595718576 on OpenAlexaffabout
D. Norris

Bibliographic record

Venue78th EAGE Conference and Exhibition 2016 · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsNalcor Energy (Canada)
Fundersnot available
KeywordsSubmarine pipelineGeologyDeep waterStructural basinPaleontologyStratigraphyMesozoicFrontierMineral resource classificationDrillingSeismologyOceanographyTectonicsArchaeologyGeographyGeochemistry

Abstract

fetched live from OpenAlex

Summary The Southern and South-eastern Newfoundland and Labrador regions cover an area approximately one-third the size of the US Gulf of Mexico. This vast offshore region remains underexplored both on the shelf, and in the slope and deep water area. Additionally, the slope and deep water areas historically have only partial seismic coverage with acquisition in 1999 (Western Geophysical Petrol Ltd.) and 2000 (Geco Geophysical Canada Ltd.). As a result of this sparse seismic dataset, Mesozoic basin extents have not been fully delineated over the slope and deep water regions. Recently, Nalcor Energy partnered with TGS and PGS, has acquired regional 2D seismic surveys over these frontier offshore regions in an effort to better understand their resource potential. Prior to the recent acquisition of 36,325 line kilometres of regional 2D broadband seismic that began in 2014 little information existed about the extent and nature of the stratigraphic section over the remaining portion of the Southern Newfoundland offshore. Interpretation of the newly acquired seismic data is revealing increased extents of previously defined basins, thickness of Mesozoic stratigraphy, as well as the complex deformation history of these regions.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.646
Threshold uncertainty score0.999

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)0.0020.000

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 head0.015
GPT teacher head0.181
Teacher spread0.166 · 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.

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

Citations0
Published2016
Admission routes2
Has abstractyes

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