{"id":"W1503846774","doi":"10.1007/11540007_31","title":"Dynamic Modeling, Prediction and Analysis of Cytotoxicity on Microelectronic Sensors","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Toxicant; Computer science; Impulse response; Microelectronics; Cytotoxicity; Biological system; System dynamics; Dynamic data; Artificial intelligence; Chemistry; Nanotechnology; Mathematics; Materials science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003705023,0.0005732381,0.0008390681,0.0003136936,0.0002487779,0.0005893611,0.001081358,0.0009902187,0.001046281],"category_scores_gemma":[0.00158345,0.0004438178,0.0006533574,0.0003611556,0.0004927121,0.0006396622,0.0003746679,0.0005692553,0.0001447496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001062827,"about_ca_system_score_gemma":0.0004864821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009864265,"about_ca_topic_score_gemma":0.00542194,"domain_scores_codex":[0.9998661,0.0000218821,0.000005594783,0.00003959828,0.00003952874,0.00002716996],"domain_scores_gemma":[0.9992987,0.0005023693,0.00005956378,0.00003887086,0.00008520238,0.00001526403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001474038,0.00001309712,0.000323116,0.00001145794,0.000006040102,0.00001771926,0.000007201311,0.9935647,0.001679512,0.001236008,0.00008423945,0.003042098],"study_design_scores_gemma":[6.965607e-7,0.00000364644,0.0001164779,4.177442e-7,0.000001593923,0.000003081555,0.000001048449,0.9989133,0.0004621999,0.0004719229,0.0000245467,0.000001061061],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4758538,0.001382689,0.5137478,0.0006966107,0.00009941949,0.00006899847,0.0003501867,0.0006227507,0.007177813],"genre_scores_gemma":[0.9905162,0.0002875555,0.005988575,0.0000265596,0.00001413238,0.00003826501,0.00007977212,0.00002238599,0.00302648],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009864265,"threshold_uncertainty_score":0.01961368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005923594525652127,"score_gpt":0.2211117635306458,"score_spread":0.2151881690049937,"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."}}