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Record W4313816528 · doi:10.18280/ijdne.170609

Forest Stand Reproduction in the Changing Climate Conditions on the Example of the Bashkortostan Republic

2022· article· en· W4313816528 on OpenAlexvenueno aff
Regina Baiturina, Владимир Коновалов, Aydar Gabdelkhakov, Elvira Khanova, Dina Anvarovna RAFIKOVA

Bibliographic record

VenueInternational Journal of Design & Nature and Ecodynamics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsReforestationReproductionAgroforestryForest managementSilvicultureTree breedingSelection (genetic algorithm)GeographyBiologyEcologyForestryWoody plant

Abstract

fetched live from OpenAlex

The paper considers the issues of forest reproduction by plantation type based on selection and genetic approaches in forest cultivation and forest seed production. The research explores the growth condition and regularities of common pine plus-trees, the best individuals with high-quality genetically determined traits for creating seed and vegetative seed plantations and further conducting breeding work are identified. There are findings on the genetic variability of trees on forest-seed plantations by ISSR markers on the example of common pine, identifying the existing gene pool of this species. Studies of forest reproduction by plantation type conducted throughout one republic or the planet as a whole provide preconditions for its improvement and higher efficiency of silvicultural works. Scientific and environmental institutions and authorities should join their efforts to develop effective measures to compensate for reforestation forest management.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.251
Teacher spread0.235 · 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
Published2022
Admission routes1
Has abstractyes

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