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Record W173424

Improving the efficiency of bayesian network based edas and their application in bioinformatics

2013· article· en· W173424 on OpenAlexaff
Robin Gras, Elham Salehi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicBayesian Modeling and Causal Inference
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsEDASBayesian networkEstimation of distribution algorithmComputer scienceHeuristicMathematical optimizationProbabilistic logicBenchmark (surveying)Machine learningArtificial intelligenceBottleneckBayesian probabilityMathematics
DOInot available

Abstract

fetched live from OpenAlex

Two isoenzymes of carbonic anhydrase, one with high activity and the other with low activity, were isolated from rat gastric tissue. It was found that both isoenzymes were phosphorylated in vitro by protein kinase isolated from gastric mucosa and that this process was stimulated by 3',5'-AMP. The phosphorylation of highly active carbonic anhydrase isoenzyme is shown to result in the increase of its activity. The phosphorylation of low active carbonic anhydrase isoenzyme did not affect its activity or decreased it slightly. The results obtained suggest that the activation of carbonic anhydrase in vivo by gastrin (pentagastrin), histamine and 3',5'-AMP is due to the phosphorylation of highly active isoenzyme by 3',5-AMP-dependent protein kinase. It seems possible that in this process histamine and 3',5'-AMP act as sequential mediators of pentagastrin effect.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.980
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2013
Admission routes1
Has abstractyes

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