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Record W2004195896 · doi:10.1139/e04-046

Magnetic and gravity models across terrane boundaries in southern New Brunswick, Canada

2004· article· en· W2004195896 on OpenAlexaffvenueabout
M. S. King, Sandra M. Barr

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2004
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsAcadia University
Fundersnot available
KeywordsTerraneGeologyPaleontologyBasementTectonicsArchaeologyGeography

Abstract

fetched live from OpenAlex

Regional magnetic and gravity map patterns, combined with modeling of magnetic and gravity data constrained by measured magnetic susceptibility and density information, are used to develop a better understanding of the three-dimensional character of major lithostratigraphic subdivisions (terranes) in southern New Brunswick. Models and long wavelength map patterns indicate that the boundaries between the Caledonia, Brookville, Kingston, and New River terranes are sub-vertical or dip slightly to the southeast, and that some map units in each terrane extend to depths ranging from 5000–8000 m. The Mascarene and St. Croix terranes are less well constrained than the more outboard terranes on the models, in part because of limited physical property data but also because of the presence of the Saint George Batholith, which obscures relationships among stratigraphic units. The models are consistent with the Mascarene terrane being a cover sequence over the boundary between the St. Croix and New River terranes, the specific location and nature of which is now revealed by the geophysical models. The terranes appear to be associated at depth with geophysically distinct bodies that are interpreted to correspond to the basements, from southeast to northwest, of Avalon terrane sensu stricto, Brookville–Bras d'Or terrane, and Exploits–Gander composite terrane. The latter "Gander basement" underlies the Kingston, New River, Mascarene, and St. Croix terranes.

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 score0.259
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.222
Teacher spread0.203 · 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

Citations26
Published2004
Admission routes3
Has abstractyes

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