Bibliographic record
Abstract
A project that began as an undergraduate co-op research project is now developing and commercializing proprietary extracts of Artocarpus altilis (breadfruit) as novel sources of cosmetic raw materials. A method to produce proprietary cosmetic ingredients from breadfruit male inflorescences was established for industrial scale with collaborators in developing countries. The proprietary ingredient was found to have antioxidant activity, a desirable property for skin care formulations and has been formulated into five skin care products for the Altilis Beauty product line. A second proprietary product stream is being developed from breadfruit leaf extracts as a potential source of squalene. Currently, squalene is extracted from the livers of sharks and used as a moisturizer by the cosmetics industry. There is an expected increase in demand by the cosmetic, food and pharmaceutical industries combined, with a total of 5,300 tons (6 million sharks) required per year by 2022. Several brands including L'Oreal, Unilever and Estee Lauder phased out of using shark-sourced squalene in 2006 – 2008 replacing it with squalene extracted from olives but the yield is relatively low and there is still a high demand for shark squalene as a cosmetic ingredient. To replace shark squalene with squalene from a sustainable plant source, methods of high yield extraction need to be developed and the quality and purity need to be established. With support from the Fuel Injection Program with Innovation Guelph, a new sustainable breadfruit skincare line, Altilis Beauty™ was launched in 2017. Further development will continue through research at UBC and the University of Guelph in collaboration with Soleluna Cosmetics Inc.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".