{"id":"W2988284534","doi":"10.1007/s00500-019-04458-6","title":"Proposing a new model to aggregate ratings in multi-source feedback approach based on the evidence theory","year":2019,"lang":"en","type":"article","venue":"Soft Computing","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Aggregate (composite); Computer science; Context (archaeology); Discounting; Process (computing); Data mining; Transformation (genetics); Machine learning; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.01468745,0.0003554718,0.0005129934,0.0005243423,0.0003496303,0.001076345,0.002141799,0.0001146012,0.0001083204],"category_scores_gemma":[0.02471358,0.000226826,0.0001774748,0.001741984,0.00007577803,0.000356452,0.0008232645,0.000554577,0.000755514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001383386,"about_ca_system_score_gemma":0.0002844969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005358905,"about_ca_topic_score_gemma":0.000008141581,"domain_scores_codex":[0.9937693,0.001054536,0.001160906,0.00131819,0.002021662,0.0006753658],"domain_scores_gemma":[0.9854616,0.01193728,0.0005216043,0.001508133,0.0003568676,0.0002145225],"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":[0.0001325991,0.0000605247,0.003464787,0.00001063637,0.000003303296,0.000003159769,0.004685936,0.8524904,0.003178234,0.0005161347,0.0005301911,0.1349241],"study_design_scores_gemma":[0.0006299597,0.00004819525,0.001521116,0.001006096,0.00000378577,0.000004508995,0.001159423,0.9923112,0.0003639146,0.002453614,0.0001974298,0.0003007724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3815369,0.0000537789,0.6156945,0.001066829,0.000198476,0.0007918912,8.241716e-7,0.00007744636,0.0005793944],"genre_scores_gemma":[0.7927559,2.307804e-7,0.2018077,0.003842994,0.00007352847,0.000006262063,4.546933e-7,0.00003805307,0.001474887],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4138868,"threshold_uncertainty_score":0.9999607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2612364840829234,"score_gpt":0.4111398158365485,"score_spread":0.149903331753625,"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."}}