{"id":"W3042599237","doi":"10.1142/s0219622020500273","title":"An Invasive Weed Optimization-Based Fuzzy Decision-making Framework for Bridge Intervention Prioritization in Element and Network Levels","year":2020,"lang":"en","type":"article","venue":"International Journal of Information Technology & Decision Making","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Weighting; Computer science; Fuzzy logic; Bridge (graph theory); Operations research; Genetic algorithm; Tardiness; Bridge maintenance; Mathematical optimization; Artificial intelligence; Engineering; Machine learning; Deck; Mathematics; Routing (electronic design automation)","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":[0.0004637722,0.0001766859,0.0002646325,0.0008957991,0.00007857821,0.0001967331,0.0004606553,0.0002314225,0.00002717129],"category_scores_gemma":[0.002029252,0.0001787016,0.00008725508,0.0004577921,0.00003239172,0.001449345,0.00006732024,0.000364375,0.000002066088],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002370906,"about_ca_system_score_gemma":0.00005829395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.243756e-7,"about_ca_topic_score_gemma":0.00000481505,"domain_scores_codex":[0.9980271,0.00001871878,0.00120291,0.0001271799,0.0004096417,0.0002144926],"domain_scores_gemma":[0.9978825,0.0004504573,0.0005851107,0.0001171947,0.000912271,0.00005243416],"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":[0.0001756202,0.000008601551,0.002696401,0.00002128024,0.00003396117,0.000006040709,0.0002439288,0.7217222,0.0000573526,0.002252477,0.0001126375,0.2726695],"study_design_scores_gemma":[0.002208972,0.000304361,0.01323997,0.003882815,0.00002842294,0.00005442166,0.0007675455,0.8910009,0.0007602557,0.08676044,0.0006730361,0.0003188994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07691061,0.0001224459,0.9208521,0.0002498996,0.001452123,0.0002832559,0.0000154162,0.00008691374,0.00002720032],"genre_scores_gemma":[0.6415938,0.00002808476,0.3577992,0.000275146,0.0002667664,0.00001436967,0.000009308635,0.00001332838,6.494991e-8],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5646831,"threshold_uncertainty_score":0.7287241,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009413240744325509,"score_gpt":0.2906676684817182,"score_spread":0.2812544277373927,"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."}}