Estimation of Economic Value of Gardening Produces Hidden Harvest (Case Study: Prunus Persica)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Due to growing population and needs more food supply, increased productivity in agricultural production have been more considered and for this purpose, different strategies such as increasing acreage, yield per unit area, achieving superior cultivars, field operations management and the like have been suggested by the researchers. One of the ways (strategies) is that lower hitherto been considered, reduce postharvest losses, or harvest. Plant produces are living systems: due to doing postharvest biological processes that concluded to be ruined quickly. Harvesting and postharvest handling of crops, play a critical role in assuring their price and quality. Peach is perishable produce and after harvest a high percentage of it is useless immediately. Improvement of postharvest quality and efficiency in the marketing system necessitates improved harvesting methodologies, training of farmers, as well as the use of appropriate facilities and equipment for transportation, packaging and storage. So in this study for estimation the economic value peaches hidden harvest was used benefit-cost method. The required data were collected with through a questionnaire from 45 peach growers of east Golestan province. The results of this investigation disclosed that use of appropriate facilities and equipment for transportation, packaging, storage and increasing the awareness of farmers will be increased peach produce with reducing losses till 40 percent in the region. It is suggested measures such as precooling and cool keeping till the time of selling or processing, using refrigerated vehicles, equipping sales centers to refrigerators, proper packaging and maintenance of fruit at a temperature of 2 to 3 o C should be implemented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it