Applications for Food Safety in Istanbul Level of Recognition by Consumers
Bibliographic record
Abstract
The aim of this study demographic characteristics of consumers, to identify the relationship between living standards and food shopping habits, determine the level of awareness by the MFAL of measures taken to ensure food safety and to develop recommendations in this context.Research is the largest city in Turkey and 13.7 million people live in Istanbul, made in the first quarter of 2013. Proportional sampling method was used in this study. Making process of sampling margin of error of 1.4% and the 95% confidence interval studied. In addition to the unknown probability value of the subject on the values of p and q are considered to be 0.5. 2106 as a result of the calculations according to these data, the sample size was determined as t. Chi-square analysis of the data, Visual Relationship analysis (TIA), and logistic regression analyzes were used.Consumers are average age 38.32, college graduates 46.3%, family population 3.4, number of children 2.6, the average family income 2.495 TL/month, average food expenditure 610 TL/month, Consumers are the most purchase from supermarkets that red meat (45.3%), chicken meat (56.5%), milk (70%), dairy products (74.8%) while they purchase fresh fruit and vegetables from district market (47.3%), while food most of their attention to freshness and expiration date, reliable information for the food they receive a large proportion of TV and the internet have been identified. MFAL for public health policies, bread, salt and bran rates and arrangements for the school milk program and school canteens located right by consumers and supported. In some applications (increasing the amount of control, establishment of ALO Food Line and implementation arrangements for the sale of pesticides) and are no longer seen by consumers largely underreported.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".