{"id":"W2079263048","doi":"10.1139/l02-063","title":"Comparison of fuzzy set ranking methods for implementation in water resources decision-making","year":2002,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Water resources management and optimization","field":"Engineering","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Ranking (information retrieval); Computer science; Context (archaeology); Rank (graph theory); Data mining; Fuzzy logic; Fuzzy set; Set (abstract data type); Machine learning; Operations research; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02691409,0.001273502,0.001466961,0.005356038,0.0006810565,0.002685675,0.001286719,0.001391542,0.002497467],"category_scores_gemma":[0.04809726,0.0004521206,0.001543402,0.00389533,0.0009394331,0.002256961,0.001293921,0.001495242,0.0004665878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002202221,"about_ca_system_score_gemma":0.001452475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002590959,"about_ca_topic_score_gemma":0.003317871,"domain_scores_codex":[0.9642554,0.02690537,0.0008695765,0.000511843,0.0070134,0.0004443394],"domain_scores_gemma":[0.9632142,0.03124204,0.000947267,0.001147197,0.003256409,0.0001928673],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001024857,0.0004318684,0.002634638,0.001249509,0.0008801956,0.0000969505,0.0005971617,0.2903178,0.002376908,0.0512942,0.001980112,0.6471158],"study_design_scores_gemma":[0.0003473234,0.001972136,0.005879824,0.0004670205,0.0003613385,0.0001796478,0.0006315747,0.9355085,0.004840334,0.04230948,0.007290985,0.0002117702],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1206017,0.005876185,0.8487983,0.0009489236,0.0002545376,0.0009187315,0.0001763421,0.0004576331,0.02196759],"genre_scores_gemma":[0.4016862,0.001922264,0.5939677,0.0001050614,0.0000535655,0.0007156856,0.0001187223,0.00007726146,0.001353458],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02691409,"threshold_uncertainty_score":0.142337,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02249041824028328,"score_gpt":0.299653632726826,"score_spread":0.2771632144865427,"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."}}