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Record W4321020195 · doi:10.1017/9781009177078.013

Regional Distribution of Ferrar Magmatic Centers

2023· book-chapter· en· W4321020195 on OpenAlexaboutno aff
B. D. Marsh

Bibliographic record

VenueCambridge University Press eBooks · 2023
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyDistribution (mathematics)Mathematics

Abstract

fetched live from OpenAlex

Ferrar-style dolerites are found throughout the Transantarctic Mountains, running some 4,000 km across Antarctica; extending to Tasmania and South Africa to the Karoo system. An unusual characteristic of the Ferrar system is the extreme paucity of dike swarms that might well be expected in rifting systems. Major dike swarms are found in Canada and somewhat in the Eastern North American Rift System, but not so in Antarctica. The concentrated center of magmatic activity exhibited in the Dry Valleys is similar to what is found along ocean ridges, which is characterized by a series of spatially distributed major magmatic centers with underlying magmatic mush columns. Magma is produced and moves upward through the mush column and is distributed laterally, up and down the ridge by high level flow. The Ferrar may also be like this, as indeed most magmatic systems are, and then, where are the other such centers along the Transantarctic Mountains? The Dufek complex is clearly once such center and others may exist at various places along this range; there is telltale evidence at many locations. And it is remarkable that, by and large, there are always four major sills at most locations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.003

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 head0.028
GPT teacher head0.173
Teacher spread0.145 · 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 source (direct Gemma or distilled Codex), 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
Published2023
Admission routes1
Has abstractyes

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