The Proposed Method of Risk Analysis and Profit Estimation for Exporters of Vegetables and Fruits from Jordan
Bibliographic record
Abstract
This paper aims at developing an effective method of risk analysis and estimating export profitability of vegetables and fruits from Jordan, as a case study. The method analyzes the marketing chain between the farm gate and the first receiver at destination market. Through this process exporter may reduces the risks of the export shipment. The impact of these risks is identified in the Export Analysis Table (EAT) for Agri-business, which seeks to develop three items of marketing information including, an estimate of produce cost, selling price in destination market and additional marketing costs between the farm and first receiver in destination market. The EAT is an online tool that enables the exporter to evaluate the profitability of his shipment, who is just needed to enter the quantities of vegetables and fruits decided to be export by type of product, the expected sales and purchase prices of the products into the EAT, which directly presents the scenario of operating margin along the marketing chain and in destination markets. The EAT is an effective method to describe and guide the exporter to update and to standardize with the marketing chain requirements. Numerous products of vegetables and fruits usually export to Arab Gulf or Europe Union Markets could utilize the EAT. EAT gives a flash picture of the profitable of the export products, depending on the range of costs and sales prices given by product in each market. The EAT is intended to help the Agri-business analyze for fresh produce such as fruits and vegetables maximize export opportunities from Jordan. Key words: EAT; Export; Marketing chain; Cost; Fruit; Vegetable; Profit; Risk; Price
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".