{"id":"W2019934749","doi":"10.1016/j.coal.2010.03.004","title":"Simultaneous prediction of coal rank parameters based on ultimate analysis using regression and artificial neural network","year":2010,"lang":"en","type":"article","venue":"International Journal of Coal Geology","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":65,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial neural network; Multivariable calculus; Coal; Linear regression; Regression analysis; Regression; Mathematics; Statistics; Artificial intelligence; Computer science; Engineering; Waste management","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":[0.0002413267,0.0001039382,0.0002394986,0.0003215868,0.0000385801,0.00002603772,0.0001326408,0.0001000469,0.00003770818],"category_scores_gemma":[0.0001303398,0.00008679037,0.00009855773,0.0001070547,0.00007486653,0.00008524673,0.00001593684,0.0003301028,5.437942e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002194231,"about_ca_system_score_gemma":0.00001760344,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001627914,"about_ca_topic_score_gemma":0.00001903635,"domain_scores_codex":[0.9990726,0.00003715574,0.0004210848,0.00008820635,0.0002469624,0.000133957],"domain_scores_gemma":[0.9991956,0.0002683759,0.0002410043,0.00006039091,0.0001774857,0.00005717303],"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":[0.0002680008,0.00002000272,0.01882129,0.000007415495,0.0001992044,0.00005089779,0.00005012235,0.9695063,0.008947159,0.00001306486,0.00003313254,0.002083392],"study_design_scores_gemma":[0.0003797366,0.00009875932,0.004347471,0.00004563962,0.000121826,0.0001232512,0.00001221245,0.9930362,0.001540946,0.0001832966,0.00004844803,0.00006225018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684836,0.00004037605,0.0294896,0.0000985356,0.001767215,0.00002502613,0.000009364672,0.00001614386,0.00007009675],"genre_scores_gemma":[0.9967915,0.000007641909,0.002732505,0.00003984885,0.0004039477,3.702677e-7,0.000008715941,0.000009026871,0.000006411265],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02830789,"threshold_uncertainty_score":0.3539209,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01515649700126377,"score_gpt":0.2677238538592665,"score_spread":0.2525673568580027,"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."}}