{"id":"W4403536752","doi":"10.1145/3691620.3695258","title":"MLOLET - Machine Learning Optimized Load and Endurance Testing: An industrial experience report","year":2024,"lang":"en","type":"article","venue":"","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Machine learning","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":[0.005147253,0.000748566,0.000458813,0.00094186,0.0002703124,0.001100408,0.001505926,0.0006737338,0.002180588],"category_scores_gemma":[0.007507049,0.0003277845,0.0003308257,0.0006475297,0.0006909664,0.001466587,0.0009865612,0.001480577,0.001182553],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007957629,"about_ca_system_score_gemma":0.001081536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004853549,"about_ca_topic_score_gemma":0.003842781,"domain_scores_codex":[0.9979295,0.0006475823,0.0001175682,0.0002569249,0.0008591808,0.0001892979],"domain_scores_gemma":[0.995289,0.001909914,0.0002222503,0.0006002746,0.001624188,0.0003544223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007053324,0.001890735,0.01926211,0.0002135396,0.0001301974,0.0003386883,0.0007231298,0.1575118,0.02565087,0.004234854,0.023837,0.7655017],"study_design_scores_gemma":[0.0001690644,0.002168176,0.01292518,0.00007615939,0.00005185533,0.0003623773,0.000235765,0.9125804,0.03635075,0.002813867,0.03217415,0.00009226755],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4413172,0.001932037,0.5022045,0.003111337,0.0002882503,0.0006689734,0.001057591,0.03416234,0.01525768],"genre_scores_gemma":[0.7213374,0.0007033616,0.2654406,0.0004702749,0.0001497193,0.0001829109,0.002275441,0.00119773,0.008242531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005147253,"threshold_uncertainty_score":0.02722156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03108159318230633,"score_gpt":0.2509343598461126,"score_spread":0.2198527666638063,"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."}}