Scientific Aspects of the Formation of the Logistics System of Agricultural Companies
Bibliographic record
Abstract
The use of research in the field of logistics management of agricultural companies allows increasing the level of information access and transparency of information on the economic feasibility of logistics systems of agricultural companies in China. We need leverage to influence the dissemination of science and the ability to obtain initial data on the logistics management of agricultural enterprises. This article analyses the work of representatives of international scientific schools and Chinese scientists on research of the logistics management system in the work of agricultural companies. The main elements of scientific research, containing theoretical provisions, methodological support in the study of logistics systems of agricultural companies. The concept of scientific research in the formation of the logistics system of agricultural companies, which contains the theoretical provisions of logistics management, methodological support for monitoring the product potential of logistics systems of agricultural companies, the formation of a system of indicators of logistics systems. Scientific principles of formation of logistic system of agricultural companies are offered, which include principles of system integrity, principles of voluntariness, principles of balance, principles of adaptation to peculiarities of agricultural production, principles of increasing basic competitiveness of agricultural enterprises, principles of integration and new construction. The volumes of the main agricultural products in physical terms of China, Japan, USA, Canada, France, Germany and the main indicators of efficiency of agricultural products of China and developed countries in terms of costs, productivity, capital turnover, stock, value-added in GDP. The general conclusion of scientific research and prospects of further scientific research are formed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".