{"id":"W2807328923","doi":"10.1007/s13762-018-1813-9","title":"An integrated life cycle inventory and artificial neural network model for mining air pollution management","year":2018,"lang":"en","type":"article","venue":"International Journal of Environmental Science and Technology","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial neural network; Life-cycle assessment; Environmental science; Particulates; Gold mining; Life cycle inventory; Pollution; Air pollution; Coal mining; Inventory analysis; Emission inventory; Environmental engineering; Engineering; Waste management; Coal; Computer science; 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":[0.0006526913,0.0001201356,0.0001212802,0.0001624058,0.0002631072,0.00004317855,0.0004449509,0.0000705256,0.00009186879],"category_scores_gemma":[0.00005227618,0.0001048394,0.00002754192,0.000186879,0.00268507,0.0007210054,0.0003316302,0.0001059913,0.000003775231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004226639,"about_ca_system_score_gemma":0.0000201504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009062627,"about_ca_topic_score_gemma":0.00001868553,"domain_scores_codex":[0.998677,0.00001807156,0.0002913663,0.0002724703,0.0004563809,0.0002846706],"domain_scores_gemma":[0.9995043,0.000009490575,0.0001723326,0.0001206783,0.00001927423,0.0001738833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007049558,0.0009651216,0.2794063,0.000009818495,0.0001183715,0.00005218116,0.001717771,0.04135452,0.1145266,0.003616294,0.0008769269,0.5566511],"study_design_scores_gemma":[0.001472278,0.001954328,0.3373146,0.0000323889,0.00006770092,0.0002561379,0.005584059,0.6160683,0.005509204,0.02801752,0.003228904,0.000494595],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9954908,0.00004183226,0.002499725,0.001433776,0.0001983506,0.0001512163,0.00000818193,0.00001336364,0.0001627738],"genre_scores_gemma":[0.9937129,0.00004235412,0.005508964,0.00058946,0.00009565629,0.0000058439,0.000001747866,0.000007397929,0.00003566221],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5747138,"threshold_uncertainty_score":0.9893252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009908625303685374,"score_gpt":0.2591740423111137,"score_spread":0.2492654170074284,"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."}}