Bibliographic record
Abstract
In 1850, Theodore Gobley, working in Paris, described a substance, 'lecithine', which he named after the Greek 'lekithos' for egg yolk. Adolph Strecker noted in 1862 that when lecithin from bile was heated, it generated a new nitrogenous chemical that he named 'choline'. Three years later, Oscar Liebreich identified a new substance, 'neurine', in the brain. After a period of confusion, neurine and choline were found to be the same molecule, and the name choline was adapted. Lecithin was eventually characterized chemically as being phosphatidylcholine. In 1954, Eugene Kennedy described the cytidine 5-dihphosphocholine pathway by which choline is incorporated into phosphatidylcholine. A second route, the phosphatidylethanolamine-N-methyltransferase pathway, was identified by Jon Bremer and David Greenberg in 1960. The role of choline as part of the neurotransmitter acetylcholine was established by Otto Loewi and Henry Dale. Working in the 1930s at the University of Toronto, Charles Best showed that choline prevented fatty liver in dogs and rats. The importance of choline as an essential nutrient for human health was determined in the 1990s through controlled feeding studies in humans. Recently, an understanding of the role of genetic variation in setting the dietary requirement for choline in people is being unraveled.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.030 |
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".