{"id":"W4396677783","doi":"10.1080/17480930.2024.2348877","title":"Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions","year":2024,"lang":"en","type":"article","venue":"International Journal of Mining Reclamation and Environment","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Truck; Productivity; Wind speed; Turbine; Computer science; Random forest; Environmental science; Meteorology; Engineering; Automotive engineering; Machine learning; Geography","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":[0.002047926,0.0008699599,0.0005339775,0.001118031,0.00028808,0.0008466016,0.0007083208,0.0007225994,0.001635929],"category_scores_gemma":[0.008646736,0.0002555957,0.0007263349,0.0007369221,0.0004293716,0.001452258,0.000697126,0.000777036,0.00009792463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001332077,"about_ca_system_score_gemma":0.0008183495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006951477,"about_ca_topic_score_gemma":0.004718219,"domain_scores_codex":[0.9995283,0.0002447596,0.00003733863,0.00008918842,0.00005972076,0.0000408406],"domain_scores_gemma":[0.9935279,0.005380032,0.0003721775,0.0001999576,0.0004102216,0.0001095997],"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":[0.00007047762,0.00004671589,0.003769921,0.00002738862,0.00004039177,0.000033475,0.00004775139,0.9760786,0.0001897946,0.002694484,0.000207335,0.01679367],"study_design_scores_gemma":[0.000002729606,0.00001434829,0.0002591722,0.000001544708,0.000004178666,0.000001922324,0.000004711634,0.9981483,0.00008738435,0.001453561,0.00001916558,0.000002946161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5779296,0.0002603281,0.4181087,0.0003765647,0.00004440624,0.0001065451,0.0004981764,0.0006072471,0.00206844],"genre_scores_gemma":[0.9771957,0.00004878972,0.02213253,0.00002247803,0.00001325983,0.0000433427,0.0001952503,0.00000806697,0.0003405216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006951477,"threshold_uncertainty_score":0.01382202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02776269648886424,"score_gpt":0.2711062960216187,"score_spread":0.2433435995327544,"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."}}