{"id":"W4362579294","doi":"10.1007/s00500-023-08040-z","title":"Period estimate of wood buildings employing soft modelling techniques","year":2023,"lang":"en","type":"article","venue":"Soft Computing","topic":"Wood Treatment and Properties","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Firefly algorithm; Particle swarm optimization; Genetic algorithm; Artificial neural network; Computer science; Frame (networking); Metaheuristic; Soft computing; Algorithm; Mathematical optimization; Artificial intelligence; Mathematics; Machine learning","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":[0.0003212126,0.0003022824,0.0003388274,0.0007556126,0.0002036818,0.0005090587,0.0003751708,0.0004303359,0.00140762],"category_scores_gemma":[0.001265406,0.0002114329,0.0004768787,0.000604914,0.0001598511,0.0004991726,0.0003362298,0.0004041373,0.0003357427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003633912,"about_ca_system_score_gemma":0.0003131515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002957041,"about_ca_topic_score_gemma":0.003721845,"domain_scores_codex":[0.9999049,0.00002438404,0.000004211277,0.0000205506,0.00002492572,0.00002113203],"domain_scores_gemma":[0.9995208,0.0002548269,0.0000751299,0.00005860958,0.00006298633,0.00002756523],"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":[0.0002638464,0.0000364802,0.01026815,0.00007116619,0.00006753596,0.00007294054,0.0000357029,0.929737,0.007506525,0.003627884,0.0005772033,0.04773561],"study_design_scores_gemma":[0.000002433988,0.00001450253,0.005509854,0.000004979106,0.000009234893,0.00001390427,0.00001015114,0.9913914,0.001556439,0.001261037,0.0002196169,0.000006382053],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5290925,0.0004361538,0.4653171,0.0001033819,0.00004088197,0.00001559243,0.0006425016,0.0003245386,0.004027357],"genre_scores_gemma":[0.9795992,0.00007709799,0.01905523,0.000007660098,0.00001430713,0.000009680455,0.0002961173,0.00003403324,0.0009065113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002957041,"threshold_uncertainty_score":0.005879641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02419925957930605,"score_gpt":0.2451105257965209,"score_spread":0.2209112662172148,"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."}}