Bibliographic record
Abstract
International AgroScience Conference (AgroScience-2020) 10 April 2020 Cheboksary, Russian Federation The Conference AgroScience-2020 was jointly organized by Chuvash State Agricultural Academy (Russia), Life Science University of Bradford (United Kingdom), Grodno State Agrarian University (Republic of Belarus), and Research Institute of Livestock and Feed Production (Republic of Kazakhstan). The purpose of AgroScience-2020 was to bring together scientists, academicians, practitioners, and professionals from manufacturing sector in the fields of agro-science, agro-engineering, and agro-technology. The conference program was structured to encourage mutual inspiration and fruitful debate among researcher. Participants were offered the chance to contribute to the conference in various roles, to demonstrate novel results, and to exchange new ideas and application experiences with each other. AgroScience-2020 was mainly emphasized on (1) Actual issues of production and processing of agricultural products; (2) Actual problems of livestock and veterinary medicine; (3) Agroengineering: state and prospects; and (4) Economics and management: challenges and directions of development. We are especially grateful to our Participants and Institutions for their contribution in the event. All the manuscripts included to the Proceedings went through intensive reviews by experts in in the fields of agro-science, agro-engineering, and agro-technology from Philippines, Brazil, Egypt, Ukraine, Canada, Russia, Italy, China, Poland, Pakistan, Mexico, India, Indonesia, Spain, Latvia, Syria, Saudi Arabia, Yemen, USA, Argentina, United Arab Emirates, France, Sudan, Brunei, Malaysia, and Columbia. The Editors appreciate the enthusiasm of all Reviewers and authors to improve the quality of the papers. Web page of the AgroScience-2020: https://pasd-conf.ru/ Organizing Committee: Dr. Anton Stepanov, Laboratory of Hematological Research, Chuvash State Agricultural Academy, Cheboksary, Russian Federation Editors of the Special Issue AgroScience-2020: Prof. Andrei Mardaryev, School of Chemistry and Biosciences, Faculty of Life Science University of Bradford, United Kingdom Dr. Anna Godymchuk, Chemistry Environmental Laboratory, Tobolsk Complex Scientific Station, Ural Branch of the Russian Academy of Science, Russian Federation Dr. Natalia Karakchieva, Laboratory of Chemical Technologies, National Research Tomsk State University, Russian Federation
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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 | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.608 | 0.430 |
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".