{"id":"W3112111484","doi":"10.1109/mie.2020.2964053","title":"Using Artificial Intelligence in Mining Excavators: Automating Routine Operational Decisions","year":2020,"lang":"en","type":"article","venue":"IEEE Industrial Electronics Magazine","topic":"Belt Conveyor Systems Engineering","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Motion Metrics International (Canada); Vancouver Native Health Society","funders":"","keywords":"Excavator; Engineering; Payload (computing); Process (computing); Troubleshooting; Real-time computing; Computer science; Artificial intelligence; Automotive engineering; Reliability engineering; Computer security; Mechanical engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004746694,0.0003345828,0.0004328834,0.0002286799,0.00008077007,0.0001235245,0.0003168233,0.0002886184,0.00006678989],"category_scores_gemma":[0.0006107224,0.0003945142,0.00007696621,0.001218895,0.00002457721,0.0003096595,0.00004639443,0.0008874106,0.00007423564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004751113,"about_ca_system_score_gemma":0.0002503188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001573038,"about_ca_topic_score_gemma":0.00005424754,"domain_scores_codex":[0.9975181,0.00006376972,0.0009858661,0.0003664088,0.0003555714,0.0007102852],"domain_scores_gemma":[0.999218,0.0002271565,0.00008148315,0.0002138034,0.00006272333,0.0001968534],"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":[0.00002123792,0.00001754451,0.0001513248,0.00001674969,0.00003724524,0.000025789,0.0003202426,0.9133263,0.06708685,0.001986729,0.0002871701,0.01672279],"study_design_scores_gemma":[0.0003129389,0.00006902612,0.00002190659,0.0001421878,0.00001501923,0.00002219338,0.00006648239,0.9786025,0.01920376,0.000107581,0.001053278,0.0003831555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.718819,0.0004627328,0.2773229,0.0002965632,0.001409073,0.0004700567,0.00001589719,0.0006789379,0.000524869],"genre_scores_gemma":[0.9927621,0.00001494585,0.005462,0.00005701116,0.001566676,0.00002325673,0.00001410072,0.0000926131,0.000007233899],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2739432,"threshold_uncertainty_score":0.9998507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1175545762169128,"score_gpt":0.2830559004819377,"score_spread":0.1655013242650249,"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."}}