Estimation of Economic Value of Gardening Produces Hidden Harvest (Case Study: Prunus Persica)
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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".