{"id":"W4412505622","doi":"10.52436/1.jutif.2025.6.3.4689","title":"Comparative Analysis of Supervised Learning Algorithms for Delivery Status Prediction in Big Data Supply Chain Management","year":2025,"lang":"en","type":"article","venue":"Jurnal Teknik Informatika (Jutif)","topic":"Customer churn and segmentation","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Computer science; Big data; Supply chain; Supply chain management; Machine learning; Artificial intelligence; Algorithm; Data mining; Business","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007149112,0.0002018297,0.0004639666,0.002345119,0.0001866647,0.0002213433,0.0003875781,0.00006798656,0.00006267946],"category_scores_gemma":[0.00004266255,0.000195854,0.0001328825,0.002528181,0.00004483754,0.002119081,0.0002660227,0.000191075,0.0000226057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001227347,"about_ca_system_score_gemma":0.00004338843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004715027,"about_ca_topic_score_gemma":0.0004012446,"domain_scores_codex":[0.998145,0.000021527,0.0008807462,0.0002448908,0.000357876,0.0003499705],"domain_scores_gemma":[0.9989514,0.0000876711,0.0004000508,0.0003365624,0.0002054502,0.00001891262],"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":[0.001018759,0.0005221381,0.1656351,0.002186832,0.004149658,0.00000817297,0.004582671,0.03779782,0.0005201052,0.007038029,0.01472953,0.7618112],"study_design_scores_gemma":[0.002390423,0.00002614646,0.1488575,0.0001515325,0.0009948866,2.853037e-7,0.01015209,0.7776397,0.0001163212,0.0001002037,0.05935256,0.0002182794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8921709,0.0003511304,0.05404784,0.0004124033,0.001258918,0.002154182,0.0004622389,0.000207391,0.04893498],"genre_scores_gemma":[0.9935811,0.0001226941,0.000995861,0.0005568763,0.0001647624,0.00006515827,0.003927112,0.000009746554,0.0005766741],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7615929,"threshold_uncertainty_score":0.7986693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05596801529021032,"score_gpt":0.291022405319149,"score_spread":0.2350543900289387,"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."}}