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Record W2358846952

Experimental Teaching Example of Polymerase Chain Reaction

2014· article· en· W2358846952 on OpenAlexaff
LI Hong-mi

Bibliographic record

VenueNorthwest Medical Education · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetics, Bioinformatics, and Biomedical Research
Canadian institutionsScience North
Fundersnot available
KeywordsPolymerase chain reactionProcess (computing)Computer scienceQuality (philosophy)PolymeraseScheme (mathematics)Mathematics educationChain (unit)Polymerase chain reaction optimizationComputational biologyChemistryBiochemistryPsychologyBiologyMathematicsDNAPhysicsNested polymerase chain reactionGene
DOInot available

Abstract

fetched live from OpenAlex

Polymerase chain reaction is a powerful tool for in vitro amplication of specic nucleic acid sequences and has been assigned one of the important content of the conventional molecular biology experiments as its wide usage in life science related subjects and its impact on the scientic community involving in basic research and practical analysis.The author provide a practicable teaching scheme focusing on the quality-oriented education within which problem discussion was properly introduced into the course during running polymerase chain reaction and/or the quench time of electrophoresis.The whole process of this experiment includes problem preparation,teacher's explanation,students' operation,problem discussion,observation and analysis of the experiment result.Teaching practice proved that this scheme can effectively use the interval time,through problem discussion,to stimulate the interaction and communication between teachers and students,help students of to understand the principle and related theoretical aspects of polymerase chain reaction,enhance the students' ability of using theoretical knowledge to analysis the actual problem.Learning interest can be also promoted.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.027
Threshold uncertainty score0.090

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0270.007

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 head0.013
GPT teacher head0.311
Teacher spread0.298 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations0
Published2014
Admission routes1
Has abstractyes

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