{"id":"W4392387514","doi":"10.18280/ria.380115","title":"Machine Learning Prediction Model: A Case Study of Urban Transport of Medical and Pharmaceutical Products","year":2024,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Advanced Data Processing Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; 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":[0.0004280114,0.0001168097,0.0001853792,0.0001164189,0.00003495679,0.000009960031,0.0001211549,0.00006974878,0.0000266123],"category_scores_gemma":[0.00007232277,0.0001131421,0.00002199523,0.0003297623,0.00008352544,0.0001635073,0.00003545189,0.0003485141,0.000001517196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750646,"about_ca_system_score_gemma":0.00002383287,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003419989,"about_ca_topic_score_gemma":0.00001004328,"domain_scores_codex":[0.9989391,0.00002041456,0.000429962,0.0002452082,0.0002288574,0.0001364707],"domain_scores_gemma":[0.9996199,0.00005797205,0.00002648413,0.000183281,0.00004688479,0.00006546944],"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":[0.00003249633,0.0004371988,0.001516223,0.003683904,0.0000913726,0.001236888,0.01550684,0.8508462,0.01369321,0.0009630681,0.000107888,0.1118847],"study_design_scores_gemma":[0.00002877787,0.0001269308,0.000001649664,0.0002490238,0.00003257093,0.0004986539,0.0009038426,0.9390181,0.05828128,0.0001301357,0.000646421,0.00008263245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5427877,0.00361527,0.452629,0.00004388914,0.0000873596,0.0002475615,0.00002328746,0.0004616832,0.0001042037],"genre_scores_gemma":[0.9969996,0.000789438,0.00208227,0.000002107232,0.0000326794,0.00001951495,0.000009111111,0.00002908231,0.00003621381],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4542119,"threshold_uncertainty_score":0.4613803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04957898056973763,"score_gpt":0.3283038334122186,"score_spread":0.2787248528424809,"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."}}