{"id":"W3102892898","doi":"10.5267/j.dsl.2020.10.001","title":"Aggregating the results of benevolent and aggressive models by the CRITIC method for ranking of decision-making units: A case study on seven biomass fuel briquettes generated from agricultural waste","year":2020,"lang":"en","type":"article","venue":"Decision Science Letters","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Ranking (information retrieval); Data envelopment analysis; Briquette; Computer science; Rank (graph theory); Aggregate (composite); Operations research; Mathematical optimization; Data mining; Mathematics; Machine learning; Engineering; Coal; Waste management","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01085514,0.001510226,0.001191122,0.002498618,0.001057128,0.002353144,0.000775483,0.00103305,0.001099207],"category_scores_gemma":[0.01188631,0.0004521971,0.001747334,0.002064598,0.000970735,0.001350754,0.001204363,0.001184715,0.0001414436],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001971744,"about_ca_system_score_gemma":0.001641128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006367666,"about_ca_topic_score_gemma":0.006930904,"domain_scores_codex":[0.9932775,0.004354636,0.0002677256,0.000327403,0.001421126,0.0003516327],"domain_scores_gemma":[0.9923052,0.00538039,0.0004508834,0.0003466077,0.001335145,0.0001817448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0003591404,0.0002325663,0.007029463,0.0002772939,0.0001902913,0.0004084248,0.0005891641,0.903218,0.003208875,0.01054806,0.0007698959,0.07316888],"study_design_scores_gemma":[0.0000193366,0.0001536126,0.001276956,0.0000229024,0.00004876832,0.00004175085,0.000257452,0.9928142,0.001901202,0.003031905,0.0003902602,0.00004172919],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4400958,0.0003728892,0.5511122,0.0004474905,0.00006430606,0.0002894847,0.0001137354,0.0001477934,0.007356284],"genre_scores_gemma":[0.8907109,0.0001263947,0.107896,0.00002540114,0.00000971004,0.0001354284,0.00008038028,0.00002336459,0.0009924296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01085514,"threshold_uncertainty_score":0.05740815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.139636842681228,"score_gpt":0.4119943259098979,"score_spread":0.2723574832286699,"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."}}