Bibliographic record
Abstract
The study of the apoptotic process has grown exponentially since the publication of the first edition of Apoptosis Techniques and Protocols for the Neuromethods series in 1997. In this greatly updated second edition, seasoned experts describe in step-by-step detail their best state-of-the-art techniques for studying neuronal cell death. These readily reproducible methods solve a wide variety of research problems, including the detection of the key proteins involved in neuronal apoptosis (Bax protein, cytochrome-c, and caspases), the direct assessment of the role of pro-apoptotic proteins in neurons by viral infections and microinjections, and the detection of proapoptotic proteins in situ. There are also hands-on methods for the study of apoptosis mechanisms in neuronal compartments, for studying synaptosis, and for establishing gene expression profiles in neurodegenerative brain tissues by using DNA microarrays. Written by investigators who have used the techniques extensively, each protocol includes tips on avoiding pitfalls, notes on the method's advantages and disadvantages, and a critical survey of the literature. Cutting-edge and highly practical, Apoptosis Techniques and Protocols, Second Edition, offers both novice and seasoned investigators a rich panoply of the productive tools they need to unravel the molecular mechanisms of neuronal cell death
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.058 |
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".