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Record W2556247702 · doi:10.1139/cjes-2016-0132

Gravity and magnetic modelling of layered mafic–ultramafic intrusions in large igneous province plume centre regions: case studies from the 1.27 Ga Mackenzie, 1.38 Ga Kunene–Kibaran, 0.06 Ga Deccan, and 0.13–0.08 Ga High Arctic events

2016· article· en· W2556247702 on OpenAlexaffvenue
J.A. Blanchard, Richard E. Ernst, C. Samson

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological Studies and Exploration
Canadian institutionsCarleton University
Fundersnot available
KeywordsMaficUltramafic rockGeologyPlumeGeochemistryIgneous rockCrustMantle plumeLayered intrusionFlood basaltGeophysicsMagnetic anomalyPetrologySeismologyTectonicsLithosphere

Abstract

fetched live from OpenAlex

Gravity and magnetic data from the global EGM2008 and EMAG2 datasets are used to identify geophysical anomalies in large igneous province (LIP) plume centre regions with the goal of characterizing mafic–ultramafic intrusions linked to those LIPs. Geophysical anomalies within 18 LIPs distributed globally are investigated. Four of these LIPs are selected for detailed modelling: the 1.27 Ga Mackenzie, 1.38 Ga Kunene–Kibaran, 0.06 Ga Deccan, and 0.13–0.08 Ga High Arctic LIPs. We recognize three spatial distribution types for intrusions in plume centre regions. These are (1) intrusions emplaced along a circular fault system that circumscribes the plume centre, (2) intrusions emplaced along linear rifts that, in some cases, converge towards the plume centre, and (3) single or unclassified intrusions. Modelling supports that the geophysical anomalies associated with these LIPs tend to be produced by large (radius >30 km) and deep-seated crustal intrusions, with densities consistent with mafic–ultramafic rock and magnetic susceptibilities consistent with serpentinized ultramafic rock, except within the Deccan where intrusions are smaller, mainly mafic in composition, and positioned at shallower depths in the crust.

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)0.001
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 score0.748
Threshold uncertainty score0.924

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.034
GPT teacher head0.210
Teacher spread0.176 · 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

Citations35
Published2016
Admission routes2
Has abstractyes

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