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Record W2896383633 · doi:10.3997/2214-4609.201801433

Deformable Plate Reconstructions: Modelling the Relationship between Hyperextension, Basin Evolution and Flexural Uplift

2018· article· en· W2896383633 on OpenAlexaboutno aff
R.C Whittaker, Bridget E. Ady

Bibliographic record

VenueProceedings · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geophysical Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyStructural basinPassive marginEscarpmentPaleontologySubsidenceSedimentary basinGeomorphologyThermal subsidenceRift

Abstract

fetched live from OpenAlex

Summary Deformable plate reconstructions combined with thermal subsidence and flexural uplift modelling help give us a clearer understanding of the relationship between hyperextension, sedimentary basin evolution, and basin margin uplift. These new methods of evaluating hyperextended and continental margins provide input for environment of deposition interpretations and for basin modelling. We use a palinspastic deformable-margin plate reconstruction modelling method for the Central and North Atlantic, and Circum-Arctic to evaluate some emerging deep-water provinces. These include exciting new areas such as Guyana/Suriname-Mauritania/Senegal conjugate margins in the Central Atlantic, the Porcupine and Orphan Basins in the North Atlantic, and the southwest Barents Shelf and Labrador Sea circum-Arctic basins. Many of these areas share characteristic structural features associated with hyperextension such as high rates of subsidence, uplift of the basin margins, the development of submarine escarpments, perched basins, highly rotated fault blocks and localised shear zones. The structural features that characterize hyperextended margins are not well documented are often overlooked, but when the structural processes are fully understood they can provide us with potential new petroleum plays.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.712

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 studies0.0010.000
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 head0.053
GPT teacher head0.218
Teacher spread0.165 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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