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Gold deposits and mineralization studies: A 2018-2022 Scopus-based bibliometric analysis

2024· article· en· W4394809073 on OpenAlexaboutno aff
Svetlana Kamagurova

Bibliographic record

VenueBulletin Of The Mineral Research and Exploration · 2024
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsScopusMineralization (soil science)Web of scienceInformation retrievalGeographyGeologyComputer sciencePolitical scienceMEDLINESoil science

Abstract

fetched live from OpenAlex

Gold is an important source of economic development and international relations. The accumulation of this element and the formation of deposits is an urgent research problem. The variety of types of deposits, conditions of their formation, and methods of deposit development are of interest to many researchers. Thus, in order to understand the demand for this area, a bibliometric analysis was carried out using the keywords “gold deposits” and “gold mineralization” for a five-year period. The database was acquired from the Scopus and included 793 articles from 77 countries. Statistical analysis was done using the VOSviewer and Mapchart software. Among top publishing countries China, Australia, and Canada took the highest ranks. Top 3 authors stand out as having a high H-index, which indicates their high qualifications in this field. The most popular journal publishing these studies is Ore Geology Review with 259 publications. However, the most cited articles are published in Mineralium Deposita, Economic Geology, Geological Journal, Gondwana Research, Earth-Science Reviews, Geoscience Frontiers, and Geochimica et Cosmochimica Acta. All of these journals are related to Earth and planetary sciences. The large gold mining provinces of China, Australia, and Canada are a key factor in the high publication rate among researchers.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.005
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.831
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.1690.179
Science and technology studies0.0010.000
Scholarly communication0.0050.002
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.103
GPT teacher head0.349
Teacher spread0.246 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Observational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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