Raising Awareness About the Impacts of Squalene on the Well-Being of Individuals, Societies & the Environment!
Bibliographic record
Abstract
Before humans inserted themselves into the aquatic food chain, sharks were at the top maintaining balance and playing a crucial role on this earth. For hundreds of millions of years (even before the dinosaurs!) sharks have been shaping our underwater ecosystem and creating a foundation for life in all parts of the sea. Now with 95% of shark populations decreasing everywhere our health and the planet's health is at major risk. Shark livers contain an oil so hydrating and rich all cosmetic that companies want to get their hands on it. This simple substance, also known as squalene, is found all around the world in the form of cosmetics (lotions, anti-wrinkle creams, sunscreen, foundations) and daily off the shelf supplements. With the serious lack of education about what’s in our cosmetics, it makes it scary to think that almost all of us have been absentmindedly plastering on prehistoric predators on our body in the name of beauty.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".