{"id":"W2950757391","doi":"10.48550/arxiv.1903.11980","title":"Probabilistic Analysis of Facility Location on Random Shortest Path Metrics","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Complexity and Algorithms in Graphs","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; RWTH Aachen University; Fédération Wallonie-Bruxelles; Deutsche Forschungsgemeinschaft; Freistaat Sachsen; National Foundation for Science and Technology Development","keywords":"Facility location problem; Shortest path problem; Heuristic; Mathematical optimization; Probabilistic logic; Metric (unit); Mathematics; Computer science; Path (computing); Euclidean geometry; Probabilistic analysis of algorithms; Metric space; Euclidean distance; Greedy algorithm; Upper and lower bounds; Combinatorics; Discrete mathematics; Statistics; Artificial intelligence; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005840548,0.0002833321,0.0006844222,0.001266326,0.00009108711,0.00007264657,0.001798976,0.0002208113,0.00002641748],"category_scores_gemma":[0.0002157129,0.0003091533,0.0005006528,0.005391479,0.000147828,0.0001797625,0.00110045,0.0004518404,0.00003075286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000201269,"about_ca_system_score_gemma":0.0002084009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002281992,"about_ca_topic_score_gemma":0.00005815531,"domain_scores_codex":[0.9978284,0.0002264396,0.0003271271,0.001158946,0.0002129717,0.0002460974],"domain_scores_gemma":[0.9965087,0.0005500846,0.0003797835,0.002014237,0.0004424844,0.0001047348],"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":[0.00004154255,0.0002394678,0.006938095,0.00008755964,0.0005479888,0.00001702053,0.000128712,0.9063522,0.000001956014,0.08442591,0.00002720075,0.001192307],"study_design_scores_gemma":[0.0004373337,0.0000774198,0.01402935,0.00004577409,0.0006573133,2.82482e-7,0.00002130508,0.9616618,0.00002266454,0.02269368,0.00005551742,0.0002976047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2014778,0.00004009115,0.7967085,0.00002004906,0.0003574188,0.0003878629,0.0001110924,0.00009854156,0.0007986689],"genre_scores_gemma":[0.9986402,0.00005842194,0.0009714036,0.00002557737,0.00001363376,0.000001086737,0.000085411,0.000004963254,0.0001993129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7971624,"threshold_uncertainty_score":0.999936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08636848891975024,"score_gpt":0.2005369723349278,"score_spread":0.1141684834151775,"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."}}