{"id":"W3105826940","doi":"10.3390/pr8111478","title":"Quantitative Methods to Support Data Acquisition Modernization within Copper Smelters","year":2020,"lang":"en","type":"article","venue":"Processes","topic":"Metallurgical Processes and Thermodynamics","field":"Engineering","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Merck Canada Inc. (Canada); McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Automation; Process (computing); Smelting; Computer science; Data acquisition; Process control; Process engineering; Systems engineering; Engineering; Mechanical engineering; Materials science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002529565,0.000787269,0.0005600018,0.0006310561,0.000333444,0.001289533,0.001275817,0.00075478,0.001511833],"category_scores_gemma":[0.00513405,0.0004653519,0.0006638599,0.0004896053,0.001025703,0.001349968,0.0009751911,0.001142639,0.0001939023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009952731,"about_ca_system_score_gemma":0.001285727,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003754226,"about_ca_topic_score_gemma":0.002163009,"domain_scores_codex":[0.9988913,0.0004503843,0.0000754178,0.0001236394,0.0003927595,0.00006665068],"domain_scores_gemma":[0.9978131,0.001377353,0.0002198856,0.0002191161,0.000327717,0.00004276317],"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":[0.00004464662,0.00004260064,0.0006377673,0.0001138501,0.00002237957,0.00004694852,0.0001077282,0.9243862,0.009492077,0.04246136,0.000206644,0.02243791],"study_design_scores_gemma":[0.000005062713,0.00001642266,0.00009251435,0.000005093552,0.000003196431,0.000006025702,0.0000100544,0.9904456,0.002961208,0.005461888,0.0009873676,0.000005503264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006901699,0.00006330063,0.9917268,0.00007228442,0.00001316686,0.00003447012,0.00004485991,0.0004842635,0.0006592593],"genre_scores_gemma":[0.5599436,0.0002674349,0.4378353,0.00004135042,0.00002636491,0.0002536283,0.0001497089,0.0002398684,0.001242702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003754226,"threshold_uncertainty_score":0.01337779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011143590295787,"score_gpt":0.3569974577751231,"score_spread":0.2558830987455444,"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."}}