Bibliographic record
Abstract
Abstract Shortening is a commercially prepared edible fat used in frying, cooking, baking, and as an ingredient in fillings, icings, and confectionary items. It may have been named so because when dough is mixed, the water‐insoluble fat prevents cohesion of gluten strands, literally shortening them and thus generating tender baked goods. Shortening is typically a 100% fat product formulated with animal and/or vegetable oil. These oils have been processed for functionality (describes how well a product performs in a certain application) and to remove any undesirable flavor and aroma. Overall, shortening improves the texture and palatability of food products. Products with characteristics similar to shortening are discussed in this article, but only when similarities in raw material, usage, production methods, and equipment are similar to those of shortenings. Formulations include concerns about crystalline nature of the fats, fatty acid distribution, fractionation, hydrogenation, and interesterification. Manufacturing and processing equipment are detailed. The many forms of shortenings, i.e. solid, liquid, high stability, all purpose, pourable, and special formulations, are discussed. Packaging and storage of the final products are also presented.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.028 |
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".