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Record W2997912748 · doi:10.29303/emj.v1i2.37

Analisis Dependensi Faktor Makroekonomi terhadap Tingkat Harga Emas Dunia dengan Pendekatan Copula

2019· article· en· W2997912748 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEIGEN MATHEMATICS JOURNAL · 2019
Typearticle
Languageen
FieldComputer Science
TopicData Mining and Machine Learning Applications
Canadian institutionsnot available
FundersMcGill University
KeywordsCopula (linguistics)EconometricsGumbel distributionEconomicsTail dependenceMathematicsStatisticsExtreme value theoryMultivariate statistics

Abstract

fetched live from OpenAlex

Gold is a precious metal that used many times as an alternative investment. Before investing, every investor requires relevant information to make profitable investment decisions. Relevant information can be obtained by looking at the dependency relationship between variables. In identifying the relationship between variables, a Copula approach could be used, since it is not tight against the assumption of normality, which is common in macroeconomic variables. Copula used were Archimedean Copula family, such as Clayton, Frank, and Gumbel. The results of this study indicated that the Archimedean Copula of the Frank family is the best Copula models to explain the structure of dependencies between gold and each composite stock price index and exchange rate, with each parameter obtained were 2.286 and -2.2390, respectively, while Clayton Copula family was the best Copula models to explain the structure of dependencies between gold and oil, with parameter obtained was 3.4090.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score0.938

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.011
GPT teacher head0.245
Teacher spread0.234 · 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