{"id":"W2035551247","doi":"10.1287/trsc.1060.0186","title":"The Minisum Multipurpose Trip Location Problem on Networks","year":2007,"lang":"en","type":"article","venue":"Transportation Science","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Heuristic; Network topology; Service (business); TRIPS architecture; Computer science; Mathematical optimization; Set (abstract data type); Facility location problem; Integer programming; Simple (philosophy); Type (biology); Linear programming; Operations research; Mathematics; Computer network","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.002371572,0.0001079767,0.00006574354,0.0001882734,0.0007557388,0.0002183164,0.0003835938,0.00003028685,0.00004966738],"category_scores_gemma":[0.0000660665,0.00008037177,0.00003439896,0.001793397,0.000231999,0.0008717481,0.000007621918,0.00009178789,0.0002375089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003557556,"about_ca_system_score_gemma":0.00002877355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003051543,"about_ca_topic_score_gemma":0.002604,"domain_scores_codex":[0.9984262,0.00000392693,0.0003598597,0.0002972533,0.0005820938,0.0003306571],"domain_scores_gemma":[0.9992369,0.00003595941,0.00009196505,0.0002538402,0.0003605175,0.0000208517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000323674,0.0003340665,0.01889386,0.0001431616,0.00001775412,0.000006142403,0.00085745,0.1846968,0.0008691773,0.5643714,0.004200634,0.2252859],"study_design_scores_gemma":[0.0006418169,0.00002538632,0.7489671,0.00003643753,0.00002805069,1.34242e-7,0.001196896,0.1016601,0.0003251254,0.00113726,0.145648,0.0003337662],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6479367,0.0001244757,0.2915173,0.007591814,0.002974507,0.002026158,0.000002227251,0.00050914,0.04731769],"genre_scores_gemma":[0.9977254,0.00001308981,0.000241997,0.001235873,0.0001567228,0.00002307879,0.00002119245,0.000006796759,0.0005757944],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7300732,"threshold_uncertainty_score":0.5812609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02171325028169159,"score_gpt":0.2578306280513072,"score_spread":0.2361173777696156,"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."}}