{"id":"W4379054304","doi":"10.3390/mi14061174","title":"PreOBP_ML: Machine Learning Algorithms for Prediction of Optical Biosensor Parameters","year":2023,"lang":"en","type":"article","venue":"Micromachines","topic":"Advanced Biosensing Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multiphysics; Algorithm; Mean squared error; Elastic net regularization; Lasso (programming language); Computer science; Biosensor; Machine learning; Artificial intelligence; Mathematics; Feature selection; Materials science; Engineering; Statistics; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001075408,0.0001042308,0.00011074,0.00004831414,0.00008414518,0.000007775497,0.00009000256,0.00009040255,9.807364e-7],"category_scores_gemma":[0.00009019591,0.00009393876,0.00008929963,0.0001263478,0.00007024877,0.000002200711,0.00006148807,0.00005689988,0.000002601958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004589602,"about_ca_system_score_gemma":0.000009678628,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009318813,"about_ca_topic_score_gemma":0.000004099454,"domain_scores_codex":[0.9993692,0.00001501443,0.0001709253,0.000237597,0.00005146339,0.0001558158],"domain_scores_gemma":[0.9996332,0.0000260531,0.00006606519,0.0001823664,0.00005848743,0.00003384159],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003117773,0.00002680046,0.0007205239,0.00001935708,0.00002179924,3.295727e-7,0.000009796038,0.0001433892,0.9904877,0.0001044001,0.001013531,0.007421198],"study_design_scores_gemma":[0.0003012717,0.0003034834,0.001630785,0.00001204845,0.00001980465,0.00001165519,0.00001342821,0.006145773,0.9576873,0.0003616507,0.03340187,0.0001109025],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.952714,0.000150052,0.0455822,0.0003378669,0.00007793282,0.0004003785,0.0004646981,0.0001756297,0.00009727447],"genre_scores_gemma":[0.9022532,0.0001647533,0.09506759,0.00004348771,0.0001059952,0.00005841453,0.001627238,0.00003260521,0.000646749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0504608,"threshold_uncertainty_score":0.3830711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02351424550160193,"score_gpt":0.2932858076104043,"score_spread":0.2697715621088024,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}