Bibliographic record
Abstract
There are as many ways to interpret Bob Dylan’s lyrics as there are ways that a cell can die. Like Dylan’s music, the cell death field has undergone several transformations over the years. In the early days cell death could be divided into three categories based primarily on morphology: Type 1 (programmed cell death or apoptosis), Type 2 (cell death with autophagy), and Type 3 (necrosis). 1 , 2 Genetic approaches in model organisms like Caenorhabditis elegans and Drosophila uncovered the apoptotic cell death genes and ordered them into pathways, 3 , 4 while biochemical approaches, primarily in cell lines, established the molecular mechanisms by which apoptosis proteins function. Chemical approaches were instrumental in the subsequent identification and study of non-apoptotic forms of cell death, such as necroptosis and ferroptosis. 5 , 6 The two reviews in this issue of CDD cover the roads less travelled on the journey to death. They address how signals from neighbouring cells license and fine-tune the apoptotic machinery, and detail the role of lipids in non-apoptotic cell death.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.094 | 0.037 |
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".