{"id":"W2319447784","doi":"10.1139/l2012-038","title":"Optimum risk allocation model for construction contracts: fuzzy TOPSIS approach","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Construction Project Management and Performance","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Risk management; Negotiation; Process (computing); Computer science; Risk analysis (engineering); TOPSIS; Set (abstract data type); Fuzzy set; Operations research; Fuzzy logic; Management science; Engineering; Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002146444,0.0001070655,0.0001943637,0.0006875583,0.0001598151,0.0001544326,0.0002668189,0.00006232478,0.00008162647],"category_scores_gemma":[0.0008722325,0.00009494583,0.0001015158,0.0003531453,0.00003525428,0.001053139,0.000008573957,0.0001671569,0.000006010944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001130815,"about_ca_system_score_gemma":0.0002856936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007870898,"about_ca_topic_score_gemma":0.0032222,"domain_scores_codex":[0.9986947,0.00002028118,0.0005593851,0.0001062637,0.0003035854,0.0003157482],"domain_scores_gemma":[0.9986042,0.0001445851,0.0003497004,0.0001656349,0.0003477362,0.0003881088],"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":[0.00002800247,0.00001539778,0.01641748,0.00003018063,0.0001010979,9.705554e-7,0.002422693,0.8456177,0.0003225358,0.01352609,0.005554002,0.1159638],"study_design_scores_gemma":[0.0004675875,0.00002651621,0.003072955,0.00001968868,0.00005512558,0.00009466823,0.0006340302,0.9584633,0.000110586,0.001706855,0.0351597,0.0001889976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05284699,0.0003551315,0.9404587,0.0001138721,0.001503638,0.000152498,0.00001654744,0.0000105248,0.004542085],"genre_scores_gemma":[0.9568294,0.00001794634,0.0425709,0.00002567925,0.0003650521,0.000006437961,0.000001946022,0.00001054266,0.0001721086],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9039824,"threshold_uncertainty_score":0.3871779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04814154988124845,"score_gpt":0.2722476655642158,"score_spread":0.2241061156829674,"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."}}