{"id":"W7117302840","doi":"10.21590/ijtmh.06.1-2.02","title":"Predictive Analytics for Reducing Title V Deviations in Chemical Manufacturing","year":2020,"lang":"","type":"article","venue":"International Journal of Technology Management and Humanities","topic":"Environmental Policies and Emissions","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Wildlife Fund Canada","funders":"","keywords":"Predictive analytics; Analytics; Consistency (knowledge bases); Complement (music); Key (lock); Data analysis; Predictive modelling; Statistical learning","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":[0.00005248922,0.00007503521,0.0001032346,0.0001581681,0.00004682905,0.00003103828,0.0002468241,0.00005204367,0.0007469833],"category_scores_gemma":[0.00001306007,0.00007358086,0.00004484502,0.00003840739,0.0001751524,0.0001076535,0.0002380556,0.0001373085,0.00002103503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118044,"about_ca_system_score_gemma":0.00000351333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003540786,"about_ca_topic_score_gemma":0.000001732331,"domain_scores_codex":[0.9994071,0.000003406355,0.0002439205,0.00009895855,0.0001452596,0.0001014318],"domain_scores_gemma":[0.9997802,0.00001386101,0.0001278082,0.00003986778,0.000008718433,0.00002958456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008696334,0.001345516,0.02815471,0.0006343883,0.003797466,0.0005260889,0.01347553,0.01563928,0.008729309,0.2167969,0.1754707,0.5345605],"study_design_scores_gemma":[0.002922799,0.0007552674,0.02197822,0.0007041759,0.0004417849,0.00007284078,0.01736882,0.01197934,0.01848629,0.04363117,0.8810045,0.0006548382],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7710159,0.002971532,0.008962264,0.0593147,0.00181169,0.000880792,0.0001677838,0.00006735826,0.1548079],"genre_scores_gemma":[0.9957697,0.001001013,0.001344954,0.0003369923,0.000149294,0.000003160884,0.000001988913,0.000007224545,0.001385681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7055337,"threshold_uncertainty_score":0.8178943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02225112210146906,"score_gpt":0.2461737626396845,"score_spread":0.2239226405382154,"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."}}