{"id":"W2615276204","doi":"10.1504/ijlsm.2017.10005115","title":"A hybrid approach based on BOCR and fuzzy MULTIMOORA for logistics service provider selection","year":2017,"lang":"en","type":"article","venue":"International Journal of Logistics Systems and Management","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Fuzzy logic; Computer science; Selection (genetic algorithm); Robustness (evolution); Service provider; Risk analysis (engineering); Operations research; Service (business); Business; Machine learning; Marketing; Artificial intelligence; Mathematics","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.003045425,0.0001692184,0.0003299126,0.0004335476,0.0002820775,0.002047852,0.0009348194,0.00005135124,0.000006719586],"category_scores_gemma":[0.003242917,0.0001221756,0.00007765484,0.00005008999,0.00008334587,0.0002636815,0.0002545714,0.0001173628,0.000004884471],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009047204,"about_ca_system_score_gemma":0.00003698969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007420511,"about_ca_topic_score_gemma":0.00001182009,"domain_scores_codex":[0.9969813,0.0000914737,0.000949621,0.0003513744,0.001457917,0.0001683588],"domain_scores_gemma":[0.9952231,0.000672352,0.001411458,0.0003465498,0.002234414,0.000112092],"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":[0.003726956,0.001424365,0.01869116,0.001053271,0.001270223,0.0006688279,0.0005351772,0.4990292,0.0005749593,0.1430051,0.06184396,0.2681768],"study_design_scores_gemma":[0.002445321,0.0002193601,0.007956418,0.0002539772,0.00006981732,0.0001499481,0.0003613404,0.945749,0.0000179131,0.008351058,0.03423001,0.0001958778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004022918,0.00008231692,0.9878777,0.001583515,0.002689691,0.0005226462,0.00005992367,0.000009768863,0.003151563],"genre_scores_gemma":[0.9434406,0.00003381663,0.05514509,0.0004609158,0.0004462984,0.00001935332,0.000003891865,0.00001334843,0.0004366765],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9394177,"threshold_uncertainty_score":0.9989881,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2137970908944425,"score_gpt":0.4299609266409029,"score_spread":0.2161638357464604,"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."}}