Signal Amplification by the <i>trans</i>-Cleavage Activity of CRISPR-Cas Systems: Kinetics and Performance
Bibliographic record
Abstract
Functional Basics of Cas12 and Cas13 208 Nuclease Activity of Cas12a 208 Nuclease Activity of Cas13a 208 Quantitative Determination of trans-Cleavage Kinetics of Cas Enzymes 209 trans-Cleavage Kinetics of Cas12 and Cas13 212 trans-Cleavage Kinetics of Cas12 and Its Contributing Factors 212 trans-Cleavage Kinetics of Cas13 and Its Contributing Factors 212 Nucleic Acid Detection Sensitivity Achieved by trans-Cleavage Activity of Cas Enzymes 213 Relations between the trans-Cleavage Kinetics and the Limit of Detection 213 Prediction of Performance Aided by Machine Learning 214 Challenges of trans-Cleavage and Signal Amplification in Live Cells 214
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.017 |
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".