Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Oils and fats consist of triacylglycerols containing a range of fatty acids. Commodity oils are predominant sources of C 16 –C 18 fatty acids (corn, cottonseed, groundnut, linseed, olive, palm, rape, sesame, soybean, sunflower), whereas some contain short‐ and medium‐chain fatty acids (butter, coconut, palm kernel) or long‐chain fatty acids (fish, other seafoods, algal, and fungal) in various proportions. Fatty acid and triacylglycerol compositions determine the physical, chemical, and nutritional properties of oils and fats and their uses in both food and nonfood, such as oleochemical manufacture. Fatty acids generally contain only two types of reactive functional groups, the terminal carboxyl and a number of carbon–carbon double bonds. Reaction at, or modification of, these groups is central to their industrial use. Oxidation (particularly oxidative degradation of edible fats), reduction (particularly partial hydrogenation), and reactions used to produce surfactants and oleochemicals (epoxidation, ozonolysis, metathesis, sulfonation, production of nitrogen‐containing derivatives) are described. A current concern is the environmental impact of industrial chemistry and new processes that use less solvent, milder conditions, and renewable resources, and that produce less waste are required. Oils and fats are a major renewable resource, and environmental concerns may be met “through the use of enzymes and improved chemical catalysts as alternatives to current technologies.” Novel chemistry of oils, fats, and fatty acid derivatives with potential industrial application is highlighted, particularly by introducing new functionality to the alkyl chain.
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.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 it