{"id":"W4388565361","doi":"10.1007/s42461-023-00875-2","title":"Intelligent Fleet Management Systems in Surface Mining: Status, Threats, and Opportunities","year":2023,"lang":"en","type":"article","venue":"Mining Metallurgy & Exploration","topic":"Mining Techniques and Economics","field":"Engineering","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"SWOT analysis; Context (archaeology); Strengths and weaknesses; Computer science; Intelligent decision support system; Risk analysis (engineering); Data science; Operations research; Systems engineering; Process management; Artificial intelligence; Engineering; Business","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":[0.0005277952,0.0001877031,0.0002511472,0.0002808905,0.00005043347,0.0001223106,0.0000913997,0.00007700079,0.00001826854],"category_scores_gemma":[0.000006401035,0.000211885,0.00002865353,0.0001942491,0.00002421925,0.0003963376,0.00007306052,0.00007141158,0.00003199529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000902418,"about_ca_system_score_gemma":0.000009598883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002759965,"about_ca_topic_score_gemma":0.00003965177,"domain_scores_codex":[0.9988973,0.00003480152,0.0003976629,0.0002329055,0.0001079057,0.0003294922],"domain_scores_gemma":[0.9995865,0.00005195101,0.00005351098,0.0002152834,0.00001494845,0.000077787],"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":[0.00003128776,0.00004054617,0.001861793,0.0006070603,0.0002701871,0.0001653565,0.02111311,0.7947059,0.0004346598,0.01541108,0.00760527,0.1577538],"study_design_scores_gemma":[0.0003212121,0.00008909706,0.0003184727,0.000266339,0.00004422741,0.000007376271,0.03441545,0.9053249,0.0005008423,0.0008928239,0.05728895,0.0005302788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9784114,0.001765669,0.0124639,0.0001231217,0.0005458997,0.000330877,0.000008494491,0.00100174,0.005348893],"genre_scores_gemma":[0.9774433,0.01636679,0.004969719,0.00001919742,0.00004372479,0.0001226761,0.0001556108,0.0000591617,0.0008198315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1572235,"threshold_uncertainty_score":0.8640419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1241696817313126,"score_gpt":0.2645354298574331,"score_spread":0.1403657481261205,"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."}}