Bibliographic record
Abstract
How to avoid food poisoningFoods that are contaminated with germs can make you sick.The germs can cause stomach pains, diarrhea or vomiting.They can also cause more serious problems such as kidney failure, blood infection and even paralysis.Children, elderly people and people with weak immune systems are most at risk of developing problems from the germs in food.But food poisoning doesn't have to happen.You can do some really simple things to make sure that the food you serve your family is safe.• Avoid milk and fruit juices that are unpasteurized.Pasteurized foods have been through a process that kills germs.If milk and fruit juices are pasteurized, it will say so on the label.• Cook foods thoroughly, especially red meat, poultry and eggs.Cooking these foods all the way through will destroy harmful germs.• Eat foods soon after they have been cooked so that harmful germs don't have time to grow.• Foods that are not cooked before they are eaten, such as fresh fruits and vegetables, should be rinsed under running tap water.• Keep hot foods hot (60°C) and cold foods cold (4°C).You should make sure your fridge is set at a temperature of 4°C or less.• When preparing raw meats and poultry, keep them away from cooked food, fresh fruits and vegetables.Use separate cutting boards for raw meats and vegetables.• When serving leftovers, make sure that you reheat foods all the way through.• Wash your hands with hot, soapy water before and after preparing food.• Keep your kitchen clean.Use a mild solution of water and soap to clean your counters, cutting boards and utensils.• Protect your food from insects and animals.For more information, visit the Canadian Food Inspection Agency Web site at <www.cfia-acia.agr.ca> or read
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 teacher head, 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".