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Record W7104015234 · doi:10.5382/com.16.30

Mineral Deposit Exploration—Discovery Trends: 1900–2023

2025· book-chapter· en· W7104015234 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsBoomMineralQuarter (Canadian coin)Base metalWorld War IIChinaEconomic shortage

Abstract

fetched live from OpenAlex

Abstract Over the past 124 years (1900–2023), global mineral exploration has undergone dramatic changes in the number and type of discoveries made, the methods used, who made the discoveries, and where they were made. Presently, more than 15,000 significant mineral deposits are estimated to exist, and the number is increasing annually by 70 to 90 deposits. At the start of the 20th century, nearly half of all discoveries were made in just three countries—the United States, Canada, and Australia. By the 21st century these countries contributed only one quarter of discoveries, supplanted by Africa, Latin America, and China. Between 1900 and 2023, gold (37%) and base metals (25%) dominated discoveries. However, iron ore discoveries were periodically strong (1938–1970 and 2000–2013), as were those of uranium (1946–1985) and lithium (2010–2023). In the early 1900s, >90% of all discovered deposits cropped out and were located by prospectors. Subsequently, other players and discovery techniques came to the fore, especially post-World War II using airborne geophysics and after the 1960s by applying high-sensitivity atomic absorption spectroscopy to large-scale geochemical surveys. Over the past eight decades, 80 significant innovations lowered costs and improved discovery, and artificial intelligence/machine learning is expected to continue the trend. Major mining companies made 30 to 40% of all discoveries from the 1930s to the early 2000s. Governments discovered an additional 15 to 30% of all deposits. Major companies and governments have now been overtaken by junior companies, accounting for 77% of Western world discoveries in 2023. Most recently, the economic boom in China in the early 2000s increased exploration expenditure tenfold globally (2005–2012) because of the growth of metal demand, reaching US$43.8 billion in 2012 and lifting labor and drilling costs. Expanded exploration company numbers (mostly inexperienced juniors) and few high-quality exploration targets inflated the cost-per-discovery fivefold. While discovery costs have since improved, they are still more than twice pre-2005 levels. To address this, the focus of investors, exploration managers, and geologists will need to be on high-quality targets that are generally perceived to have the greatest chance of delivering high-value deposits, including in covered regions and/or adjacent to known high-grade deposits and mines.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.014
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.009
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.005

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 head0.024
GPT teacher head0.222
Teacher spread0.198 · 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
Published2025
Admission routes1
Has abstractyes

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