{"id":"W4387260777","doi":"10.20944/preprints202309.2181.v1","title":"Blockchain-enabled Pharmaceutical Supply Chain under Uncertain Demand: Cost Prediction through the Tuning of Evolutionary Supervised Learning","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Component (thermodynamics); Weighting; Supply chain; Function (biology); Ranking (information retrieval); Machine learning; Artificial intelligence; Reliability (semiconductor); Data mining; Mathematical optimization; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012307,0.0005020258,0.000535708,0.0005240996,0.0002490726,0.0005898763,0.0006013334,0.0005982196,0.0007112378],"category_scores_gemma":[0.003915255,0.0002245949,0.0003336638,0.0004680628,0.0003363672,0.001097496,0.0004633653,0.0006764969,0.00009419816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008175879,"about_ca_system_score_gemma":0.0008476057,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00668445,"about_ca_topic_score_gemma":0.004665818,"domain_scores_codex":[0.9996765,0.0001187981,0.000017401,0.00006466705,0.00008152198,0.00004119459],"domain_scores_gemma":[0.9980558,0.001245949,0.0002223603,0.0001195348,0.0003025392,0.0000538079],"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":[0.00003047633,0.000032648,0.001557279,0.00001588817,0.00001385965,0.00001532586,0.000009591185,0.9831833,0.0004025996,0.0007616918,0.000102201,0.01387524],"study_design_scores_gemma":[9.417291e-7,0.00000487618,0.0001308118,7.146222e-7,9.763713e-7,0.000001662937,9.688323e-7,0.9994884,0.0001162735,0.0002336194,0.00002016256,6.278769e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4575766,0.0003352508,0.5376844,0.0003987188,0.00003569906,0.00006019516,0.000121727,0.0002922306,0.003495177],"genre_scores_gemma":[0.9791887,0.00007286489,0.019859,0.00002474082,0.000008330971,0.00002968626,0.00008353546,0.0000123793,0.0007206439],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00668445,"threshold_uncertainty_score":0.01329106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1139985794595035,"score_gpt":0.3442120098091294,"score_spread":0.2302134303496259,"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."}}