Bibliographic record
Abstract
Milk marketing in rural area of Dhofar Region face a lot of difficulties and constrains by individual small scale farmers due to the lack of facilities and access to market. Therefore, farmers reduce their cow and camel milk production and group their animal into three or four groups to be milked in alternative days. Government Authorities decided to establish Milk Collection Centers to facilitate milk marketing and provide raw milk to Dairy industries. A risk analysis for the investment in milk collection centers on rural area of Dhofar Region was conducted in this study. The results showed that all MCC investments had a positive NPV except Shahbi Aseab Center. The study indicates there is a direct relationship between total milk collected, milk price, distant between MCC and Dairy plan and investment profitability. The study revealed an inverse relation between animal population at MCC zone and risk. The probability of achieving returns lower than the opportunity cost was highest for MCC located far from dairy plant which process and market dairy products. Risk premium for four MCC has been calculated relative to Salalah MCC (preferred location) and (Garoun Hirity MCC) was found as preferred MCC location and risk efficiency. In order to make the investment in MCC more attractive, the Government incentives need to be offered to farmers to increase milk production and improve raw milk quality. However, this approach might make investments in bulk milk collection centers feasible. Thus, a recommendable strategy for a successful modernization of the Oman dairy sectors inbound logistics would be to promote an increase in the volume of the milk produced per farm and improve marketing facilities through MCC.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".