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Record W4308447285 · doi:10.18280/ijdne.170512

Russian Timber Industry: Current Situation and Modelling of Prospects for Wood Biomass Use

2022· article· en· W4308447285 on OpenAlexvenueno aff
Sergey Medvedev, Mikhail Zyryanov, Aleksander Mokhirev, Oľga Kunickaya, Roman S. Voronov, Тамара Стородубцева, Olga Grigoreva, Igor Grigorev

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBioeconomy and Sustainability Development
Canadian institutionsnot available
Fundersnot available
KeywordsLoggingVariety (cybernetics)Biomass (ecology)AgricultureState (computer science)Wood industryCurrent (fluid)BusinessNatural resource economicsAgricultural economicsGeographyEngineeringForestryEconomicsEcologyArchaeologyMathematics

Abstract

fetched live from OpenAlex

The purpose of this article is to investigate certain aspects of the current state of the Russian timber industry. The Boston Consulting Group matrix is created to illustrate the current state of the industry in a variety of sectors. The industry's growth rates in various federal districts of the country are examined. Models of changes in volume indicators of the Krasnoyarsk Territory's timber industry output, prices, and individual qualitative characteristics for round timber are obtained. This region is fascinating due to the variety of agricultural products and climatic conditions (logging despite the High North conditions). The study recognizes the significance of expanding the use of the entire tree biomass and stimulating integration associations in the forest industry.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.029
GPT teacher head0.249
Teacher spread0.220 · 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 designSimulation or modeling
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

Citations17
Published2022
Admission routes1
Has abstractyes

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