{"id":"W3039123993","doi":"10.4018/ijhiot.2020070105","title":"Alternative Generation in Complex Decision Modelling Using a Firefly Algorithm Metaheuristic Approach","year":2020,"lang":"en","type":"article","venue":"International Journal of Hyperconnectivity and the Internet of Things","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Firefly algorithm; Benchmark (surveying); Metaheuristic; Computer science; Mathematical optimization; Set (abstract data type); Construct (python library); Algorithm; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000694213,0.0001157285,0.0002877607,0.00009340046,0.00002412746,0.00004128981,0.0003632947,0.00005780913,0.000006759766],"category_scores_gemma":[0.0001901505,0.00008524874,0.0001739605,0.00007291877,0.0001080328,0.0000372678,0.0001580801,0.0001587301,2.022128e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000280089,"about_ca_system_score_gemma":0.00003158614,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002198219,"about_ca_topic_score_gemma":0.000007431906,"domain_scores_codex":[0.9988134,0.0001663334,0.0004439612,0.0001797061,0.0003107132,0.00008591992],"domain_scores_gemma":[0.9991079,0.00007381695,0.0003965852,0.00008330125,0.0002906031,0.000047785],"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":[0.002032495,0.0002501608,0.00110261,0.00002182699,0.001824622,0.00003778442,0.004077366,0.8518203,0.1119916,0.001206723,0.0001638677,0.02547061],"study_design_scores_gemma":[0.001349455,0.00009984485,0.00006039214,0.00002548332,0.00007028801,0.000144089,0.0001225217,0.9845278,0.01210729,0.001205469,0.0002057955,0.00008162299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5450871,0.0004338971,0.4541501,0.0001891297,0.00007259082,0.0000403212,0.00000197687,8.250997e-7,0.00002403046],"genre_scores_gemma":[0.9667414,0.0001575621,0.03241515,0.000296653,0.0003634315,0.000001013379,0.000009627383,0.000009944075,0.000005152991],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.421735,"threshold_uncertainty_score":0.3476343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04667345722033376,"score_gpt":0.2726082047840562,"score_spread":0.2259347475637224,"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."}}