{"id":"W4294225733","doi":"10.7717/peerj-cs.1068","title":"An introduction of preference based stepping ahead firefly algorithm for the uncapacitated examination timetabling","year":2022,"lang":"en","type":"article","venue":"PeerJ Computer Science","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Initialization; Computer science; Domain (mathematical analysis); Heuristic; Preference; Firefly algorithm; Mathematical optimization; Particle swarm optimization; Algorithm; Artificial intelligence; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01770018,0.0001262563,0.0002129628,0.0006419831,0.00200029,0.0004344937,0.002019842,0.00002391672,0.00009349998],"category_scores_gemma":[0.001345424,0.00009134605,0.00009012978,0.004068346,0.0004339592,0.0007920165,0.0002250759,0.000196,0.000007424007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009630643,"about_ca_system_score_gemma":0.0003686835,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004511953,"about_ca_topic_score_gemma":0.000003744775,"domain_scores_codex":[0.9957342,0.0003638085,0.0005402114,0.0007910422,0.002197695,0.0003730828],"domain_scores_gemma":[0.9955359,0.001682278,0.0003588553,0.0009434494,0.001385937,0.00009364168],"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":[0.000008584255,0.0001222632,0.00009247586,0.000002296013,0.000007327389,2.375599e-7,0.0008558606,0.2798449,0.004657584,0.001058714,0.0003968388,0.712953],"study_design_scores_gemma":[0.0002582361,0.0002481209,0.008212372,0.000003189385,0.00001722229,0.00000744274,0.0003128883,0.9842823,0.002460155,0.001398831,0.002683907,0.0001153067],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07629953,0.00006119822,0.9198722,0.001625265,0.001699045,0.0003170203,0.00003314509,0.00007213301,0.0000204597],"genre_scores_gemma":[0.7487735,4.846927e-7,0.2507113,0.0001037409,0.0002351069,0.00005057149,0.00001166048,0.000006044767,0.0001076338],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7128376,"threshold_uncertainty_score":0.999299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.183252322804375,"score_gpt":0.3671684286047902,"score_spread":0.1839161058004152,"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."}}