{"id":"W3215727787","doi":"10.1016/j.ijmst.2021.11.009","title":"Environmental impact improvements due to introducing automation into underground copper mines","year":2021,"lang":"en","type":"article","venue":"International Journal of Mining Science and Technology","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Glencore (Canada); Laurentian University","funders":"Laurentian University; Mitacs","keywords":"Automation; Life-cycle assessment; Work (physics); Environmental science; Environmental impact assessment; Range (aeronautics); Productivity; Global warming; Global-warming potential; Environmental engineering; Engineering; Mining engineering; Production (economics); Greenhouse gas; Climate change; Ecology; Oceanography; Geology","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":[0.0002141402,0.00006041539,0.00008494519,0.0004814176,0.00006381274,0.00009546364,0.0001902121,0.00003743194,0.00004358765],"category_scores_gemma":[0.0002549748,0.00005495526,0.00001582494,0.0003043821,0.0000878895,0.0004919092,0.00004837641,0.0000752468,0.000005318042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001944285,"about_ca_system_score_gemma":0.000118976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001712701,"about_ca_topic_score_gemma":0.00001012562,"domain_scores_codex":[0.9992697,0.000004130957,0.0002143116,0.0001019352,0.0003151022,0.00009478664],"domain_scores_gemma":[0.9995193,0.00002402505,0.00007030235,0.00005612065,0.0002802419,0.00004994616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000007139715,0.00003007403,0.003774561,0.000004484922,0.0000506415,0.00005105217,0.0006353108,0.001684413,0.9052754,0.0003235622,0.001322602,0.08684076],"study_design_scores_gemma":[0.001667525,0.0005990493,0.04469183,0.0002246784,0.00004055525,0.006590674,0.009936988,0.02680394,0.8800086,0.004467214,0.02440717,0.0005618276],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9935989,0.0001983341,0.003079224,0.002168959,0.0006375158,0.0000193544,0.000001265921,0.00002982079,0.0002665851],"genre_scores_gemma":[0.9953043,0.00005374878,0.00438969,0.0001116535,0.0000763854,0.000001374283,9.85034e-7,0.000004409302,0.00005746482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08627894,"threshold_uncertainty_score":0.224101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006059291218926916,"score_gpt":0.2770085293955087,"score_spread":0.2709492381765818,"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."}}