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Record W4415164396 · doi:10.55186/25880209_2025_9_1_19

DOMESTIC AND INTERNATIONAL EXPERIENCE OF LEASE RELATIONS ON FOREST FUND LANDS

2025· article· en· W4415164396 on OpenAlexaboutno aff
Irina Zolina

Bibliographic record

VenueInternational Agricultural Journal · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsLeaseRentingForest managementCommunity forestryCertified woodIllegal loggingControl (management)

Abstract

fetched live from OpenAlex

The article examines the domestic and international experience of lease relations on forest lands. Lease relations on forest fund lands are of particular importance for Russia. They can provide the country with huge additional funds to the federal and local budgets. In a market economy, long-term rental relationships should be a priority. The organization of effective use of forest lands is a powerful factor in the socio-economic development of the country. The article describes the measures necessary to lease a forest (land) plot, and provides links to regulatory documents. Information about the federal executive authority responsible for control and supervision in the field of forest relations (with the exception of forests located in specially protected natural areas) is displayed. Despite the differences in the principles of forest management on the lands of the forest fund of Canada, Sweden and Finland, some of them, such as forests for the people, tourism, and the use of water resources in forest areas used in other countries, it is extremely necessary to apply in the Russian forestry system.

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)0.002
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 score0.013
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.004
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0130.001

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 head0.013
GPT teacher head0.282
Teacher spread0.269 · 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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