{"id":"W2099583394","doi":"10.5267/j.msl.2013.06.022","title":"ATM cash management using genetic algorithm","year":2013,"lang":"en","type":"article","venue":"Management Science Letters","topic":"Scheduling and Optimization Algorithms","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Cash; Genetic algorithm; Algorithm; Cash management; Business; Finance; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001771646,0.0001653663,0.00009839399,0.0003500787,0.0002073898,0.0003034991,0.0004723191,0.00002025352,0.0001574052],"category_scores_gemma":[0.000001686946,0.0001700287,0.00003650648,0.0008967379,0.0001516271,0.0003688504,0.0001424867,0.00007418861,0.0003954996],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001568672,"about_ca_system_score_gemma":0.000001644086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001959394,"about_ca_topic_score_gemma":1.394054e-7,"domain_scores_codex":[0.998489,0.000009358249,0.0001949155,0.0003375459,0.000467479,0.0005016616],"domain_scores_gemma":[0.9994825,0.000005644605,0.00002588903,0.0003569153,0.00001523657,0.000113806],"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":[2.10654e-7,0.00001224085,0.0002878755,0.00004511691,0.00003797421,0.00004274531,0.00007511724,0.8615654,0.0005927812,0.0001185964,0.001939292,0.1352827],"study_design_scores_gemma":[0.0001990056,0.000004423443,0.009227213,0.00002185623,0.00002359038,0.000005257167,0.0001729452,0.9890747,0.0002579701,0.00003851387,0.0007267009,0.0002478017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1023001,0.00005242827,0.8903672,0.0004663134,0.0009786342,0.000486362,8.707157e-7,0.0004292681,0.004918813],"genre_scores_gemma":[0.050749,0.00006340457,0.9471386,0.001632831,0.00007985141,0.00005377417,0.000001970502,0.00003009315,0.000250448],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1350349,"threshold_uncertainty_score":0.6933567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008819749481747557,"score_gpt":0.2070507609996484,"score_spread":0.1982310115179009,"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."}}