Bibliographic record
Abstract
This article looks at the experience that people around the world have had with fat and is a rebuttal to Gary Taubes' piece in The New York Times 'What if Fat Doesn't Make You Fat'. Taubes fails to distinguish between different fats in his lengthy article, essentially an apologia for the Atkins' diet. But only when we know the specific fats that people are eating can we draw consistent lessons about fat. The healthiest diets, as it turns out, the diets of the Inuit, Japanese, and Greeks, provide a rich amount of the essential omega-3 polyunsaturated fatty acids, fats that are found in the photosynthetic membranes of plants, and in animals who eat those leafy greens. The least healthy diets, the diets associated with the highest rates of heart disease, diabetes and obesity, are rich in saturated fats and/or omega-6 fatty acidsand not so rich in omega-3s. Omega-6 fatty acids originate in the seeds of grains and nuts and are just as essential for human health as omega-3s. But once technology made it possible to extract these fats from corn, soy beans, and sunflower seeds, omega-6s have flooded the food supply and the human body and are now suspected of causing a long laundry list of unwanted consequences, including obesity. The key to a healthy diet is not its cholesterol content, as this trip around the world makes clear, nor its animal fat content, but rather its polyunsaturate content: a balanced ratio of omega-6s to omega-3s.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.051 |
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".