Possibilities for improvement of fruit production in Serbia
Bibliographic record
Abstract
Based on the number of bearing trees and realized production in investigated period (2000-2009) in fruit production in Serbia, the most important fruits are plums, apples, and cherries. With an average production of 482,000 tones, plums contribute 44.90% of total fruit production followed by apples (19.20%), and sour cherries and raspberries with an average share of 7.55% each. Analysis of the investigated period reveals a tendency of the fruit production increase. Trend of increase was especially evident in plum production (rate of change 9.81%), followed by apple (7.42%), apricot (7.31%), peach (6.83%) and cherry 6.64%. From 2010 to 2013, the Ministry of Agriculture, Forestry and Water Management of Republic of Serbia adopted measures through the National Program of Agriculture for the development of fruit and viticulture production. The measures primarily relate to the production and distribution of planting material, cultural technology with special emphasis on organic production, logistics, quality and standards for packaging. At this time, there is a great opportunity for the adoption of quality production from the choice of certified planting materials and modern variety selections to revolutionize this branch of agriculture. Serbia has many natural advantages for fruit production: the spatial and biological diversity, favorable climate conditions, and our tradition in the fruit production. A considerable interest among fruit farmers, steady government support through incentives and integration through cooperatives (associations) could translate into significant results.
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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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".