Bibliographic record
Abstract
Abstract Biological membranes are composed largely of proteins and lipids. A wide range of molecular structures exists among these molecules. It is therefore not surprising that biological membranes are not uniform but rather cluster certain molecules in specific regions or domains. This behavior can be mimicked in model systems comprising a limited number of components to study in more detail the molecular nature of this domain formation. Two types of domains exist that have been more extensively studied. One is a membrane domain enriched in polyanionic lipids, such as phosphatidylinositol diphosphate. Such domains are formed by the presence of proteins with segments containing several cationic amino acid residues. Such proteins have been termed “pipmodulins.” Another kind of domain is formed as a consequence of the nonuniform distribution of cholesterol in the membrane. Caveolae represent one type of cholesterol‐rich domain that is well characterized. Other cholesterol‐rich domains are termed “rafts.” Characterization of rafts in model membranes is well documented, but the nature of cholesterol‐rich domains in biological membranes remains a subject of controversy. Several imaging and fluorescence methods are being employed to further characterize the size and lifetime of small raft domains in biological membranes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.009 |
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".