{"id":"W3210410995","doi":"10.22214/ijraset.2021.38570","title":"Failure Rate Prediction of Belt Conveyor Systems using 2-parameter Weibull distribution","year":2021,"lang":"en","type":"article","venue":"International Journal for Research in Applied Science and Engineering Technology","topic":"Belt Conveyor Systems Engineering","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Pacific Railway (Canada)","funders":"","keywords":"Weibull distribution; Conveyor belt; Failure rate; Reliability engineering; Shape parameter; Scale (ratio); Computer science; Belt conveyor; Component (thermodynamics); Engineering; Mechanical engineering; Statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002197587,0.0001197353,0.0002020003,0.001159771,0.00009057995,0.0001471073,0.0004433007,0.0001471314,0.000001825071],"category_scores_gemma":[0.0005038303,0.0001278639,0.00002685353,0.001246004,0.000194114,0.0002020447,0.0001228118,0.0005545454,9.451892e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005582643,"about_ca_system_score_gemma":0.0001456995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006956761,"about_ca_topic_score_gemma":0.000002052422,"domain_scores_codex":[0.9983271,0.00001340168,0.0004063428,0.0002230927,0.0005710569,0.0004590265],"domain_scores_gemma":[0.9987584,0.0001485059,0.00004274975,0.0001606523,0.0007987454,0.00009093308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000009320416,0.00001385585,0.000219013,0.0001329799,0.00004816267,0.00002903065,0.00004750697,0.1421258,0.8359947,0.0194832,0.0001073598,0.001789117],"study_design_scores_gemma":[0.0005308716,0.00003125728,0.0002713402,0.000274006,0.000004771859,0.0005134715,0.000299455,0.907785,0.08405865,0.0007905273,0.005310229,0.0001304507],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8292618,0.00066216,0.1666766,0.0003011421,0.002430891,0.0003449213,0.00007854316,0.0001701907,0.00007373761],"genre_scores_gemma":[0.9964473,0.0000921044,0.003242616,0.000001746901,0.0001225181,0.00005681246,0.0000101758,0.00002066744,0.000006051122],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7656592,"threshold_uncertainty_score":0.5214139,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057345179160172,"score_gpt":0.3269096203102823,"score_spread":0.2763361685186805,"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."}}