Selection of gluten-free food from the perspective of diagnosed celiac
Bibliographic record
Abstract
This diploma focuses on the selection of gluten-free foods from the perspective of a diagnosed celiac. In the theoretical part is described the history of celiac disease, anatomy and physiology of small intestine, pathogenesis, manifestation and forms of celiac disease, diagnosis, screening and complications from celiac disease. It also describes a treatment of celiac disease which shows inappropriate and appropriate aliments in gluten free diet. One chapter deals with labeling of gluten free foods in the Czech Republic and in Canada. There is also a comparison of the Czech Republic and Canada regarding health care, government and restaurants offering gluten free dishes. The aim of the research is to map the selection of gluten free foods in people with diagnosed celiac disease, the second target is to explore differences in food selection in the Czech Republic and Canada. The third objective is focused on factors influencing the choice of gluten free foods. The research was conducted through a questionnaire survey. The survey was filled in the Czech Republic by 78 celiacs and in Canada by 56 celiacs. In the Czech Republic the survey was online on page named Celiake and Mladí Celiaci on Facebook. The Canadian survey was online on page The Celiac scene on facebook and filled out by costumers in...
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".