{"id":"W2055387155","doi":"10.1080/07408170490257853","title":"An improved algorithm for solving a multi-period facility location problem","year":2004,"lang":"en","type":"article","venue":"IIE Transactions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computation; Pruning; Feature (linguistics); Computer science; Algorithm; Mathematical optimization; Facility location problem; Period (music); Mathematics; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00101258,0.0006608334,0.001049039,0.0009893526,0.0005681036,0.0008579723,0.002316918,0.001327981,0.00650992],"category_scores_gemma":[0.003263645,0.0004380539,0.0007950765,0.001266806,0.0003412832,0.001600207,0.00100477,0.001459648,0.00165327],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005103168,"about_ca_system_score_gemma":0.001385416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003263118,"about_ca_topic_score_gemma":0.004559136,"domain_scores_codex":[0.9992499,0.0001652176,0.00005524436,0.0001542636,0.0002829457,0.00009238508],"domain_scores_gemma":[0.9990619,0.0004777089,0.00006970718,0.0001564774,0.000201109,0.00003313676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001996385,0.000150575,0.0007989409,0.0002458255,0.00007183944,0.0002044457,0.0001179895,0.4314284,0.007734929,0.02938742,0.008669574,0.5209904],"study_design_scores_gemma":[0.00006062539,0.00004989635,0.000153799,0.00001417611,0.00001508406,0.0001157297,0.00001546087,0.984806,0.00156098,0.007908845,0.005288711,0.00001055172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002921254,0.00007713314,0.9952572,0.00004575338,0.0000304515,0.00004277025,0.00004584017,0.0004086068,0.001171052],"genre_scores_gemma":[0.02992401,0.00007439041,0.9682745,0.00003700724,0.0000286241,0.0001033999,0.0001726501,0.00008475324,0.001300737],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00650992,"threshold_uncertainty_score":0.02177787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243745283278811,"score_gpt":0.2363730187844424,"score_spread":0.2239355659516543,"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."}}