{"id":"W6926061713","doi":"10.21227/8m8n-t819","title":"Trailer Mass Estimation Using System Model-Based and Machine Learning Approaches","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"Bioenergy crop production and management","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Trailer; CarSim; Payload (computing); Track (disk drive); Articulated vehicle; Data acquisition","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":[0.0002093629,0.0002478233,0.0002694897,0.00002356013,0.0002249256,0.0001284382,0.0002410138,0.0001666558,0.00004948177],"category_scores_gemma":[0.00001566777,0.0001098873,0.00005422351,0.0001619846,0.00005422158,0.0001203276,0.00007300385,0.0002712292,0.00003336662],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004028509,"about_ca_system_score_gemma":0.00001533465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003737225,"about_ca_topic_score_gemma":0.00009569267,"domain_scores_codex":[0.9986611,0.00006458861,0.0002639278,0.0005778804,0.0002434933,0.0001890228],"domain_scores_gemma":[0.9995238,0.00001517403,0.0002131418,0.0001205533,0.00002141211,0.0001059147],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002018714,0.00003196562,0.000007383499,0.0002693521,0.00003257113,0.00001341166,0.000004225463,0.03117336,0.001042397,0.00001507722,0.9595735,0.00781657],"study_design_scores_gemma":[0.00006446884,0.00003622354,0.00001385153,0.00003645997,0.00007552921,0.00000569299,0.00003453216,0.3572364,0.00009679011,0.000003051259,0.6421972,0.0001998377],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003998239,0.00007043056,0.002986871,0.0003508486,0.0002773315,0.0002644568,0.9955035,0.0001171995,0.00002954897],"genre_scores_gemma":[0.00745977,0.00003110417,0.001284451,0.0001736383,0.0002842166,0.00001839884,0.990673,0.000002117877,0.0000732899],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.326063,"threshold_uncertainty_score":0.4481074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1157482001160105,"score_gpt":0.2354052332840601,"score_spread":0.1196570331680497,"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."}}