{"id":"W3025824561","doi":"10.3390/computation8020046","title":"Addressing Examination Timetabling Problem Using a Partial Exams Approach in Constructive and Improvement","year":2020,"lang":"en","type":"article","venue":"Computation","topic":"Scheduling and Timetabling Solutions","field":"Decision Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Benchmark (surveying); Computer science; Constructive; Scheduling (production processes); Heuristic; Mathematical optimization; Operations research; Process (computing); Quality (philosophy); Machine learning; 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":[],"consensus_categories":[],"category_scores_codex":[0.001690962,0.0001077877,0.0002086045,0.000237986,0.0001839795,0.000314917,0.0001124556,0.00005637354,0.000009533315],"category_scores_gemma":[0.0006646383,0.00009801905,0.00003210149,0.0009572975,0.00008212902,0.0004166241,0.00007810123,0.00013896,0.000009975476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004511589,"about_ca_system_score_gemma":0.0000720867,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003185018,"about_ca_topic_score_gemma":0.000001395951,"domain_scores_codex":[0.9981254,0.0002347656,0.0004823317,0.0004655906,0.0004993718,0.0001925635],"domain_scores_gemma":[0.9990736,0.00034465,0.0002282933,0.00008875322,0.0001717238,0.00009298978],"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":[0.00002963522,0.00008245833,0.006855038,0.00002351152,0.00001954435,0.000003112725,0.006114875,0.3603891,0.009487055,0.001228652,0.00001565582,0.6157514],"study_design_scores_gemma":[0.0005611918,0.00004330396,0.01199431,0.00002195613,0.00001596514,0.00000709343,0.001173014,0.9817145,0.0006374269,0.003705042,0.0000146755,0.0001115408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5445073,0.00007680248,0.4547617,0.0001016303,0.00005820786,0.000174106,0.000001932464,0.0000282834,0.0002900011],"genre_scores_gemma":[0.8902397,0.000001093782,0.1095802,0.00007199049,0.00007765781,0.000008005689,0.000008827044,0.000007382127,0.000005129315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6213254,"threshold_uncertainty_score":0.3997101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3593459955887414,"score_gpt":0.4124942256057006,"score_spread":0.05314823001695923,"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."}}