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Record W3196171979 · doi:10.1016/j.dib.2021.107307

Data on bulk rock compositions, geochemical and textural contrasts between central and marginal parts of dykes, and MELTS modeling of lamprophyre dykes in the Kola Alkaline Carbonatite Province (N Europe)

2021· article· en· W3196171979 on OpenAlexaff
А. А. Носова, Maya Kopylova, Л. В. Сазонова, А. А. Vozniak, Н. М. Лебедева, Galina Volkova, Ekaterina Peresetskaya

Bibliographic record

VenueData in Brief · 2021
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsUniversity of British Columbia
FundersRussian Science Foundation
KeywordsCarbonatiteGeologyGeochemistryMineralogyPetrologyMantle (geology)

Abstract

fetched live from OpenAlex

This publication is a series of datasets that accompany a manuscript on petrology of lamprophyre dykes in the Kola Alkaline Carbonatite Province (N Europe) [1]. The datasets served as the basis for interpretation of melt crystallization for lamprophyric and carbonatitic dykes in the crust, to supplement many papers devoted to mantle sources and melting parameters of these rocks based on radiogenic isotopes and trace elements. The first dataset contains bulk major and trace element compositions of the dykes in three areas, Kandalaksha, Kandaguba and Turiy Mys, along with supplementary information on sampling locations and dyke characteristics. The second dataset represents photos, major and trace element plots illustrating extreme mineralogical and textural heterogeneity and layering observed in some dykes. The photographs include field photographs, optical and electron microscopy shots for thin sections in central and marginal parts of the dykes. They should be viewed together with Harker and REE diagrams illustrating the changes from dyke margins to dyke cores. The third and fourth datasets are output tables from Ryolite-MELTS software used to model crystal fractionation of the dykes from the parental melts and various P, T, fO2, XCO2, XH2O parameters. One table shows compositions of evolved melts, while the other lists crystallizing phases.

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 score0.383
Threshold uncertainty score0.674

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)0.0000.000

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 head0.235
Teacher spread0.202 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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