Bibliographic record
Abstract
International Forestry Forum “Forest Ecosystems as Global Resource of the Biosphere: Calls, Threats, Solutions” (Forestry-2020) is our annual conference organized by Voronezh State University of Forestry and Technologies named after G. F. Morozov (Russia) in October, 23, 2020, Voronezh, Russia. The event was supported by Belarusian State Technical University (Belarus), Czech Academy of Agrarian Sciences (Czech Republic), Zvolen Technical University (Slovakia), Madrid Polytechnic University (Spain), Belgrade University (Serbia), and Research Institute of Forest Genetics, Breeding and Biotechnology (Russia), and the Federal Forestry Agency of the Russian Federation (Russia). Forestry-2020 is an important event in the calendar for practitioners, academicians and young scientists across Russia, Viet Nam, Slovakia, Latvia, Germany, Kyrgyzstan, Greece, France, Poland, Romania, Spain, Kazakhstan, Bulgaria, Mexico, Moldova, Serbia, Sweden, and the Republic of Belarus. The Conference allows researchers to present their research in forestry and to develop the collaboration between institutions in forest science, management and industry. The participants of the Conference presents papers covering (1) Forestry, forest management and multi-purpose use of forests; (2) Genetics, selection, seed production, reproduction and biotechnology in forestry; (3) Biodiversity of forest ecosystems, protection and defense of forests; (4) Forest park and landscape architecture; (5) Economics and management in the forestry complex; (6) Historical and social aspects of nature management; (7) Forest industry and mechanization of the forest complex; and (8) Innovations in the forestry complex. The Organizing Committee is highly grateful for the contribution of the Participants, Institutions and Sponsors supporting the event. All the manuscripts included to the Proceedings went through intensive reviews by experts in various scientific areas from China, Italy, Indonesia, United Kingdom, Russia, Spain, India, Finland, Mexico, USA, Canada, Hungary, Lithuania, Ukraine, Iraq, Argentina, Brazil, Uruguay, Iran, Czech Republic, Portugal, Germany, Croatia, Colombia, Colombia, and Bulgaria. The Editors expresses profound gratitude to all the reviewers, whose enthusiasm amplified the high quality of the papers. The conference was supported by the Federal Forestry Agency (Russia). Web page of the Forestry-2020: https://vgltu-conference.wixsite.com/forestry2020 Organizing Committee: Dr. Svetlana Morkovina, Voronezh State University of Forestry and Technologies named after G.F. Morozov, Russia Editors of the Special Issue Forestry-2020: Prof. Antonio García-Abril, Universidad Politécnica de Madrid, Spain Dr. Anna Godymchuk, National Research Tomsk Polytechnic University, Russia Dr. Natalia Karakchieva, National Research Tomsk State University, Russia
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.636 | 0.451 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".