Bibliographic record
Abstract
The primary dietary source for trans-fats is processed food from ―partially hydrogenated oils." In November 2013, the U.S. Food and Drug Administration (FDA) made a preliminary determination that partially hydrogenated oils and it considered as no longer generally Recognized as Safe (GRAS) in human food. Before 1990, very little attention was known about how trans- fat can harm your health. In the 1990, research began identifying the adverse health effects of trans-fats. Based on these findings, FDA instituted labeling regulations for trans-fat and consumption has decreased in the US in recent decades, however some individuals may consume high levels of trans-fats based on their food choices.Trans-fats are easy to use, inexpensive to produce and last a long time action. Trans-fats give foods a desirable taste and texture. Many restaurants and fastfood outlets use trans-fats to deep-fry foods because oils with trans-fats can be used many times in commercial fryers. Several countries (Denmark, Switzerland, and Canada) and jurisdictions (California, New York City, Baltimore, and Montgomery County, MD) have reduced or restricted the use of trans- fats in food service establishments.Trans- fats raise your bad low density lipoprotein (LDL) - cholesterol levels and lower your good high density lipoprotein (HDL) - cholesterol levels. Eating trans-fats increases your risk of developing heart disease and stroke. It’s also associated with a higher risk of developing type two-diabetes.Trans- fats can be found in many foods, including fried foods like doughnuts, and baked goods including cakes, pie crusts, biscuits, frozen pizza, cookies, crackers, and stick margarines and other spreads. Look for trans fat on the ingredient list on food packages. You can determine the amount of trans- fats in a particular packaged food by looking at the Nutrition Facts panel. However, products can be listed as ―0 grams of trans fats‖ if they contain 0 grams to less than 0.5 grams of trans- fat per serving. You can also spot trans- fats by reading ingredient lists and looking for the ingredients referred to as ―partially hydrogenated oils. Small amounts of trans- fats occur naturally in some meat and dairy products, including beef, lamb and butter fat.The American Heart Association recommends cutting back on foods containing partially hydrogenated vegetable oils to reduce trans-fat in your diet and preparing lean meats and poultry without added saturated and trans-fat. Read the Nutrition Facts panel on foods you buy at the store and, when eating out, ask what kind of oil foods are cooked in. Replace the trans-fats in your diet with mono-unsaturated or polyunsaturated fats. The American Heart Association recommends that adults who would benefit from lowering LDL cholesterol reduce their intake of trans-fat and limit their consumption of saturated fat to 5 to 6% of total calories.Here are some ways to achieve that: 1. Eat a dietary pattern that emphasizes fruits, vegetables, whole grains, low-fat dairy products, poultry, fish and nuts. Also limit red meat and sugary foods and beverages. 2. Naturally oils are occurring in unhydrogenated vegetable oils like canola, safflower, sunflower and olive oil, must used. 3.Look for processed foods made with unhydrogenated oil rather than partially hydrogenated or hydrogenated vegetable oils or saturated fat. 4. Use soft margarine as a substitute for butter, and choose soft margarines (liquid or tub varieties) over harder stick forms. Look for ―0 g trans-fat‖ on the Nutrition Facts label and no hydrogenated oils in the ingredients list. 5. Doughnuts, cookies, crackers, muffins, pies and cakes are examples of foods that may contain trans-fat. Limit how frequently you eat them. 6. Limit commercially fried foods and baked goods made with shortening or partially hydrogenated vegetable oils. Not only are these foods very high in fat, but that fat is also likely to be trans-fat.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.296 | 0.220 |
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".