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Record W3001456945 · doi:10.15407/geotech2020.31.090

"STRATEGIC MINERAL RESOURCES" - THE LEADING FACTOR OF MINERAL RESOURCES POLICY

2020· article· en· W3001456945 on OpenAlex
G. V. Zemskov

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenueGeochemistry of Technogenesis · 2020
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsMineral resource classificationMineralBusinessNatural resource economicsFactor (programming language)EconomicsChemistryGeologyGeochemistryComputer science

Abstract

fetched live from OpenAlex

Against the background of the conceptual approach to the mineral resource problem, the concept of “strategic mineral resources” is generally characterized in terms of the leading factor in the mineral resource policy at the State level. The essence of this category of mineral resources and its pivotal position in the general scheme for solving the problem are revealed. It is emphasized that the problem of mineral resources is predetermined by a constantly existing contradiction between the vital necessity of the systematic consumption of mineral resources by a Human (community, state) and the restrictive access to them. The object of study is formulated by the authors of the work as the "Mineral Resource Poli-cy of the State", and the subject of the study is “Strategic Mineral Resources as the most significant part of the consumed mineral resources of the nation, considered as the leading factor in the mineral resource policy”. Consideration of the mineral resource policy on the example of a number of countries (USA, China, Russia, EU countries, Canada, Japan, etc.) shows that, although each of them is unique in this sense and has its own priorities, there are, at the same time, some similarities in understanding this problem and the ways to solve it. Herein lies a number of provisions, the analysis of which allows us to state that they represent the largest elements of efficiency ("tools") of the mineral resource policy of the "advanced" States which were developed in the process of practical activity. The studies show that the "decisive link" here is formed by the provisions related to the notion "strategic" in respect of certain types of mineral raw materials, as well as their "criticality" in terms of "supply risk" and "vulnerability from limited supply" from foreign sources. Thus, based on these empirical conclusions, it is possible to designate a “strategic line” and the main steps in solving the mineral resource problem. The decisive factor in this case, as the authors believe, is the correct allocation of the “strategic” status to the most important part of the mineral resources consumed by the nation, which allows us to create on this basis a high-ranking instrument of mineral resources policy.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.725

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 science0.0010.000
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 head0.026
GPT teacher head0.222
Teacher spread0.196 · 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