{"id":"W4390481051","doi":"10.1016/j.jclepro.2023.140459","title":"Smart dispatching for low-carbon mining fleet: A deep reinforcement learning approach","year":2024,"lang":"en","type":"article","venue":"Journal of Cleaner Production","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Greenhouse gas; Truck; Productivity; Carbon footprint; Reinforcement learning; Engineering; Computer science; Automotive engineering; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000720219,0.0001152055,0.0001763848,0.0001802733,0.0000515001,0.00007995006,0.00008178242,0.00005990164,0.000007167273],"category_scores_gemma":[0.00005783513,0.0001073193,0.0001060764,0.00007387804,0.00001008738,0.0002568964,0.0000148001,0.0002901412,0.000001165564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001370728,"about_ca_system_score_gemma":0.00001514291,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002052599,"about_ca_topic_score_gemma":0.000001376375,"domain_scores_codex":[0.999168,0.00001182804,0.000416811,0.0001306211,0.0001006206,0.0001720932],"domain_scores_gemma":[0.9996946,0.00002306337,0.00009692724,0.00009684078,0.00004359194,0.000044918],"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":[0.00002959618,0.000008833423,0.00009279985,0.0003500605,0.00007672625,0.0000025948,0.001549794,0.9578334,0.002152686,0.00007898902,0.00118286,0.03664164],"study_design_scores_gemma":[0.0001206777,0.0001336669,0.00003440025,0.0002369826,0.00004913401,0.0001428843,0.0004265182,0.9695682,0.004741748,0.0001414035,0.0242539,0.0001504967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7560504,0.0006559512,0.2381616,0.0001650671,0.002073326,0.0001920993,2.040579e-7,0.0002440127,0.002457381],"genre_scores_gemma":[0.9841633,0.000161859,0.0138833,0.000008124383,0.001367897,0.0000118185,0.000004078231,0.00004902438,0.0003505791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2281129,"threshold_uncertainty_score":0.4376353,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01300382294750398,"score_gpt":0.2219044743643293,"score_spread":0.2089006514168253,"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."}}