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Record W2946650523 · doi:10.15421/111919

Commercialization of the nature-resource potential of anthropogenic objects (on the example of exhausted mines and quarries)

2019· article· en· W2946650523 on OpenAlexaboutno aff
Sergii Sardak, Оleksandr Krupskyi, S. I. Korotun, D. Y. Reshetniak

Bibliographic record

VenueJournal of Geology Geography and Geoecology · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Business Development Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsCommercializationNatural resourceResource (disambiguation)Natural (archaeology)Land reclamationLivelihoodProductivityExploitation of natural resourcesEnvironmental resource managementNatural resource economicsBusinessEnvironmental planningEnvironmental scienceComputer scienceGeographyEcologyAgricultureEconomic growthEconomicsMarketing

Abstract

fetched live from OpenAlex

In this article we developed scientific and applied foundations of commercialization of the nature-resource potential of anthropogenic objects, on theexample of exhausted mines. It is determined that the category of “anthropogenic object” can be considered in a narrow-applied sense, as specific anthropogenic objects to ensure the target needs, and in a broad theoretical sense, meaning everything that is created and changed by human influence, that is the objects of both artificial and natural origin. It was determined that problems of commercialization of the natural-resource potential of anthropogenic objects are most often considered by researchers for specific objects, without having complex methodological coverage from the point of view of combining environmental, technical, economic and managerial components. When studying the substantiation of the scientific base, the authors confirmed the feasibility of the commercialization of natural-resource potential of anthropogenic objects on the example of a number of theoretical scientific studies in reclamation, reconstruction, recreation, remediation, restoration of biological productivity and economic value of land disturbed by economic activity. The considered examples of exhausted mines in the 21st century in the USA, Canada, Germany, Romania, and Poland indicate a wide range of opportunities for their commercialization. The study of the potential for commercialization of exhausted mines in the post-Soviet countries testified to the underused reserves for the commercialization of their nature-resource potential and their high potential for further development. The authors proposed the identification of anthropogenic objects on the basic livelihood spheres of society. There were identified the main system (natural, biological, technical, economic, social,managerial) and structural (subjects, trends, threats, risks, problems, challenges) factors of diagnosing the state of an anthropogenic object. A set of measures has been developed for commercialization of an anthropogenic object in functional and production activities, product policy, financial and investment spheres, pricing and sales policies, promotion, management and determination of property rights. Recommendations were provided on optimizing the management decision-making process based on a set of positivistic development principles, methods, and management functions. The study allows international organizations, state and local authorities, territorial communities, owners and potential investors to see new opportunities and make mutually beneficial decisions on the rational use of the nature-resource potential of anthropogenic objects.

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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0010.005
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.197
Teacher spread0.186 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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