Bibliographic record
Abstract
The flower industry today is a fairly dynamic international industry. Proof of this is the significant growth rates achieved in recent years in this area. Thus transportation of perishable goods is one of the most difficult types of delivery, and transportation of flowers is even more difficult. Because it is necessary not only to strictly adhere to the temperature regime, but also to preserve the appearance of such a demanding cargo. Conducted analysis of the flower industry has shown that market demand is stagnant, while supply is in surplus. In part, this is due to the fact that flowers are highly correlated with income, not being essential commodities. Although on the other hand, consumer demand is becoming more demanding and differentiated. The main factors influencing the market of floriculture products were identified and studied. It was noted that the market of floriculture products in Ukraine is relatively small and young, but promising and growing rapidly. In addition, it is one of the most complex and time-consuming, due to its features. First of all, the floriculture market is represented by a significant number of participants that have different basic and current resources, goals for the market, needs, and so on. Studies have shown that the floriculture industry can suffer huge losses, mainly due to the lack of proper infrastructure for storage and transportation, as well as due to the lack of control over the conditions of supply. Lack of visibility in supply chains leads to quality problems, which leads to product loss, product returns, rising costs, and time delays. In addition, changing consumer demands, an active lifestyle and an open economy are forcing manufacturers and suppliers to produce higher quality goods and constantly look for ways to optimize costs. The proposed conceptual model of floriculture supply chain management will make it possible to form a new infrastructure that will unite all the subjects of the floriculture market into a single system. Thus, we can say that the priority areas of infrastructure development of the floriculture market should be determined in terms of a systematic approach and consist in the interaction of elements of production, intermediary, floristic, design, marketing, financial, information and agricultural components. Each part of the chain must perform its function effectively in order to maintain the optimal conditions of the environment in which the products of floriculture are located, during its supply from the manufacturers to the final consumers. To this end, a combination of innovative technologies that help to manage the supply of floriculture products in real time through the supply chain was proposed. Therefore, in order to satisfy consumers, it is necessary to form an effective supply chain for floriculture products, all parts of which must work in a whole, so that end consumers can get high quality products.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".