Inputs Price Transmission Effect on Marketing Margins on Fisheries Products of Iran
Bibliographic record
Abstract
Volatility and instability of inputs price and products on the one hand and high marketing margins, on the other hand are the main characters of inefficient marketing of agricultural products. So in this paper we will consider the Prices Transmission of Inputs and Marketing Costs on Marketing Margin of Fisheries Products during 2004 to 2014. The variables examined in this study which were extracted from the website of Fisheries and Statistics Center of Iran, include hot and cold water fish prices (Larve and Fingerling), Fishmeal and Concentrate (inputs), transport and labor costs and amount of used inputs. The results show that Necessary and sufficient conditions for coincidence of inputs price transmission has rejected and mediators through asymmetrical transmission of input prices to retails increase marketing margin and thereby earn profits. The coincident test also in the transfer of marketing costs showed asymmetry coincidence of marketing costs. The variable of total amount of inputs that is considered as an explanatory variable to ensure assume constant returns to scale in marketing margin model, Its impact on marketing margins is incremental and statistically significant. The process trend variable coefficient also shows that market margins will increase over time. To improve this situation it is suggested to establish the Notification institutions of market.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".