Contents: Lipid Technology 6/2010
Bibliographic record
Abstract
Details of lutein settlement announced Barry Callebaut shows growth in declining chocolate market Cognis shows improvement on 2009 results New scheme to promote sustainable Allanblackia seed oil FSANZ intends to approve GM lipase Martek moves into Chinese markets Rhodia offers new range of eco-friendly personal care ingredients Danone reports 1st quarter growth in all sectors BASF upgrades food formulation site in Denmark Nisin films may protect cheese from Listeria New investment for Canadian canola in China IOI-Loders Croklaan to supply fully segregated palm oil Isotope ratio solution to milk adulteration Australian almonds become fashionable Research Highlights 137 A computational study of macroscopic properties of macromolecules with industrial interest Physicochemical characteristics and composition of Indian soybean oil deodoriser distillate and recovery of phytosterols Comparing process efficiency in reducing steryl glucosides in biodiesel Estolides from high-oleic sunflower oil soapstock for biolubricant applications Partial hydrolysis of soybean oil by PLA 1 (Lecitase Ultra) Macadamia nut cultivars from Hawaii -phytochemicals and oxidative stability Detection of adulteration of olive oil by GC analysis of the unsaponifiable fraction using a polar column Physico-chemical properties of subcutaneous, tripe, and intestinal adipose tissues from cattle Kinetic study of b-carotene and lutein degradation in oils during heat treatment Biodiesel production from Cynara cardunculus oil The effect of filtration processes on the minor components in olive oil Roasting-related changes in oxidative stability and antioxidant capacity of apricot kernel oil Continued page 2 www.lipid-technology.com
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.483 | 0.458 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".