{"id":"W4388442209","doi":"10.18280/isi.280519","title":"Refining the ISO 9126 Model for Enhanced Decision Support System Evaluation in the Manufacturing Industry","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universitas Diponegoro","keywords":"Refining (metallurgy); Manufacturing engineering; Process engineering; Decision support system; Computer science; Engineering; Data mining; Materials science; Metallurgy","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":[0.002302867,0.000203315,0.0001691721,0.0003260034,0.0002822387,0.0004020875,0.0003834699,0.0003026735,0.00001139248],"category_scores_gemma":[0.0001853535,0.0001467635,0.00007494263,0.0005716207,0.00004548329,0.003125762,0.0000293439,0.0004206611,0.0001353957],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005093453,"about_ca_system_score_gemma":0.00007668697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007184978,"about_ca_topic_score_gemma":0.00001271323,"domain_scores_codex":[0.9980225,0.00004465622,0.0008527256,0.0001057798,0.0005936692,0.0003806552],"domain_scores_gemma":[0.9990457,0.0003056412,0.0001381774,0.0003309007,0.0001407051,0.00003890895],"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":[0.00001565399,0.000003058932,0.00002914259,0.0003637809,0.00001454258,3.374314e-7,0.01046846,0.8533107,0.00003974244,0.0009050465,0.001361161,0.1334884],"study_design_scores_gemma":[0.0005454621,0.0000215102,0.001203772,0.00033245,0.00001933632,0.00001531376,0.01155031,0.9808987,0.002510553,0.001366808,0.001338716,0.0001971111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6770612,0.00001933861,0.2576998,0.00008755574,0.0007774948,0.001358085,0.00008724344,0.0007911284,0.06211817],"genre_scores_gemma":[0.9981089,0.000006453977,0.0004738663,0.00007661845,0.00006376173,0.0009492262,0.0002505569,0.00002434294,0.00004625479],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3210478,"threshold_uncertainty_score":0.598484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03901747540314268,"score_gpt":0.2691585746864297,"score_spread":0.230141099283287,"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."}}