{"id":"W3043460766","doi":"10.1016/j.autcon.2020.103348","title":"Neuro-fuzzy systems in construction engineering and management research","year":2020,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Sciences and Engineering Research Council; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Interpretability; Computer science; Fuzzy logic; Identification (biology); Maxima and minima; Convergence (economics); Machine learning; Fuzzy set; Artificial intelligence; Management science; Industrial engineering; Data mining; Engineering; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00334651,0.0004934124,0.0006939483,0.002802843,0.0008703546,0.002531317,0.0008079223,0.001707949,0.002278069],"category_scores_gemma":[0.004426997,0.0001968075,0.0005657648,0.004124221,0.001862525,0.002299682,0.0009335486,0.001205006,0.0003188198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002716975,"about_ca_system_score_gemma":0.00304316,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008693631,"about_ca_topic_score_gemma":0.01021263,"domain_scores_codex":[0.9982626,0.0007515569,0.0001350332,0.0001653991,0.000612755,0.0000726622],"domain_scores_gemma":[0.9979254,0.001358983,0.0002160802,0.00008748347,0.0003670561,0.00004504234],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005899086,0.00009530567,0.006299035,0.002918492,0.0002386108,0.0003847293,0.001275822,0.07037049,0.001071717,0.4803463,0.007113615,0.4298269],"study_design_scores_gemma":[0.00002389895,0.0001942572,0.01198056,0.004744171,0.0001594146,0.0005122661,0.003590377,0.1237279,0.001815667,0.6496144,0.2034988,0.0001383484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.05178878,0.3896098,0.3980737,0.01981256,0.002055658,0.0002802421,0.0005049157,0.0002419465,0.1376323],"genre_scores_gemma":[0.6875142,0.1613702,0.1396239,0.001835451,0.0008296797,0.0002651193,0.0002477096,0.0000243395,0.008289433],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008693631,"threshold_uncertainty_score":0.0197131,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03236979421865804,"score_gpt":0.271942178678385,"score_spread":0.239572384459727,"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."}}