Implementations of milk quota system in the European Union and the adaptation of milk quota system to Turkey
Bibliographic record
Abstract
When the countries with a developed dairy cattle breeding and milk production like Australia, Canada, Israel, Iceland, Norway, Switzerland, Japan and EU member states, are examined it is seen that most of them have implemented a milk quota system for a period of time. The aim of this research is to put forth how the milk sector in Turkey, a candidate country for EU, would be affected from the implementation or the abolition of the milk quota system in the EU when it becomes a member. In order to predict this, 2 different scenarios, where EU doesn’t abolish and does abolish the milk quota system and Turkey becomes a member in 2014, were made up in the scope of this study. After the comparison of the results on production and consumption estimated in the scenarios, it is seen that Turkey will be facing a considerable amount of deficit in milk and red meat in the following years if the quotas are decided to be continued. However, it is estimated that this deficit could be reduced or even a surplus of milk production could be possible if the milk quota system will be abolished. The results obtained from the scenarios show that increasing just the milk yield will not be sufficient to meet the demand of the increasing population. Therefore, it is obvious that the animal population shall also be increased. In addition, sheep, goat and buffalo breeding should be considered as important as cattle breeding
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".