{"id":"W2223905903","doi":"","title":"Robust Facility Location under Demand Location Uncertainty","year":2013,"lang":"en","type":"preprint","venue":"TSpace","topic":"Facility Location and Emergency Management","field":"Business, Management and Accounting","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discretization; Facility location problem; Computer science; Mathematical optimization; A priori and a posteriori; Event (particle physics); Set (abstract data type); Robust optimization; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007446405,0.000465693,0.0003710453,0.0003068983,0.0002429054,0.0005331559,0.0005407089,0.0003069887,0.002669014],"category_scores_gemma":[0.0002015718,0.0004731092,0.0001241025,0.0006300446,0.0001006835,0.0005965967,0.0008592762,0.0004410461,0.005854322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002640455,"about_ca_system_score_gemma":0.0001041619,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02572008,"about_ca_topic_score_gemma":0.002407801,"domain_scores_codex":[0.9975727,0.00004241602,0.0005558376,0.0008726957,0.0005377913,0.0004185749],"domain_scores_gemma":[0.9975863,0.00001955951,0.0002683878,0.001079985,0.001012182,0.00003363769],"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":[0.00001972374,0.0001377976,0.0005842483,0.001971067,0.00007547261,5.386825e-7,0.000264433,0.9448239,0.00002260395,0.01078807,0.03823331,0.003078818],"study_design_scores_gemma":[0.0008232539,0.00001258439,0.04897627,0.0003509612,0.000344307,5.15784e-7,0.004371522,0.786699,0.00002738553,0.01598894,0.1407342,0.001671083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4733708,0.00128995,0.4281874,0.0313223,0.006020353,0.004794823,0.00001969068,0.001666876,0.05332779],"genre_scores_gemma":[0.9884506,0.00006639744,0.0002432098,0.00189094,0.0006687545,0.0002749139,0.001096741,0.00003033632,0.007278113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5150798,"threshold_uncertainty_score":0.9997721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07055034781393353,"score_gpt":0.2836288116806191,"score_spread":0.2130784638666856,"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."}}