Bibliographic record
Abstract
RNA analysis and quantification require completely intact, nondegraded RNA samples to produce optimal results. Although nonenzymatic hydrolysis of phosphodiester bonds is favored by high temperature or pH and the presence of divalent cations (Mg 2+ , Mn 2+ ), an RNA sample is most likely to be rapidly degraded by a contaminating ribonuclease (RNase). RNases are difficult to completely remove or inactivate during RNA isolation procedures, and they may be introduced into the sample inadvertently during its handling. There are several possible sources for RNases in the laboratory. RNases are ubiquitous in the environment, and are found on pollen, dust, and fingerpaint grease. Routine lab procedures, such as ribonuclease protection assays or degrading RNA in plasmid preparations, introduce highly purified, concentrated RNases. RNase may be in the powdered reagents used to make the stock solutions or in the tips and tubes used for handling the RNA. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.057 |
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".