Bibliographic record
Abstract
International Scientific and Practical Conference “Forest ecosystems as global resource of the biosphere: calls, threats, solutions” (Forestry-2019) continued a series of events that started in 2008. The event was jointly organized by Voronezh State University of Forestry and Technologies named after G. F. Morozov (Russia), Madrid Polytechnic University (Spain), Belgrade University (Serbia), Belarusian State Technical University (Belarus), Czech Academy of Agrarian Sciences (Czech Republic), Zvolen Technical University (Slovakia), and Research Institute of Forest Genetics, Breeding and Biotechnology (Russia), in October, 23-24, 2019, Voronezh, Russia. Forestry-2019 welcomed practitioners, academicians and young researchers from different disciplines with an interest in forest to present research and state-of-the-art knowledge and theories in forestry and to strengthen collaboration between institutions in forest science, management and industry. The Conference explored the research 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 notable experts in various scientific areas from Italy, Poland, Argentina, Russia, Spain, Romania, Mexico, Czech Republic, USA, China, Hungary, Sweden, UK, India, Kazakhstan, France, Austria, Thailand, Germany, Canada, Italy, Latvia, Turkey, Australia, Portugal, Malaysia, Ukraine, South Africa, Japan, Belgium, Indonesia, and Korea. The Editors expresses profound gratitude to all the reviewers, whose enthusiasm amplified the high quality of the papers. The conference was supported by the Russian Foundation for Basic Research and Federal Forestry Agency (Russia). Web page of the Forestry-2019: https://vgltu-conference.wixsite.com/forestry2019 Organizing Committee: Dr. Svetlana Morkovina, Voronezh State University of Forestry and Technologies named after G.F. Morozov, Russia Editors of the Special Issue Forestry-2019: Dr. Anna Godymchuk, National University of Science and Technology “MISIS”, Russia Dr. Liudmyla Rieznichenko, F.D. Ovcharenko Institute of Biocolloidal Chemistry of National Academy of Science of Ukraine, Ukraine Dr. Antonio García-Abril, Universidad Politécnica de Madrid, Spain
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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 | 0.002 | 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 | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.591 | 0.392 |
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".