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Record W4248844807 · doi:10.1385/1-59259-233-3:105

Ribonuclease Inhibitors

2003· review· en· W4248844807 on OpenAlexaff
Brittan L Pasloske

Bibliographic record

VenueHumana Press eBooks · 2003
Typereview
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsRegistered Nurses' Association of Ontario
Fundersnot available
KeywordsRibonucleaseRNAPhosphodiester bondRNase PBiochemistryBiologyChemistry

Abstract

fetched live from OpenAlex

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 (Mg2+, Mn2+), 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.088
GPT teacher head0.321
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations5
Published2003
Admission routes1
Has abstractyes

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