{"id":"W2955988175","doi":"10.4018/ijeach.2019070101","title":"A Fuzzy Markup Language-Based Approach for a Quality of Location Inference as An Environmental Health Awareness","year":2019,"lang":"en","type":"article","venue":"International Journal of Extreme Automation and Connectivity in Healthcare","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Markup language; Correlation; Set (abstract data type); Computer science; Frame (networking); Quality (philosophy); Fuzzy inference; Inference; Fuzzy logic; Fuzzy set; Degree (music); Data mining; Artificial intelligence; Natural language processing; Fuzzy control system; Mathematics; Adaptive neuro fuzzy inference system; XML; World Wide Web","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":[0.001530638,0.00009121306,0.000224,0.0001085654,0.00004673834,0.00002214237,0.0001613424,0.00005750378,0.00003218744],"category_scores_gemma":[0.000245596,0.00008961485,0.00004697707,0.00009097391,0.00004906155,0.0003665532,0.00003353268,0.0001233256,0.000001977494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003583968,"about_ca_system_score_gemma":0.0001169521,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608471,"about_ca_topic_score_gemma":0.0002151736,"domain_scores_codex":[0.9983894,0.0002978255,0.0005742752,0.0001757325,0.0004370585,0.0001256839],"domain_scores_gemma":[0.9987748,0.0002886789,0.0006800081,0.0001070022,0.00006348753,0.00008605626],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003810395,0.0003692563,0.8796405,0.0001906108,0.00001511992,9.669662e-7,0.003097316,0.008452999,0.002415005,0.0008698558,0.000004645303,0.1045627],"study_design_scores_gemma":[0.001509829,0.0005593878,0.9161563,0.0001780346,0.00000353171,0.00001651054,0.002689019,0.07667445,0.0006077437,0.001435569,0.00002538727,0.0001442783],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9821693,0.00007867687,0.01609216,0.001146831,0.0001779654,0.0002508448,0.00002427068,0.00001001885,0.00004995887],"genre_scores_gemma":[0.9965134,0.00001100562,0.003154694,0.0002138455,0.00004743764,0.00001009047,0.00003310005,0.00000667607,0.000009785995],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1044184,"threshold_uncertainty_score":0.3943245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06656793293232724,"score_gpt":0.3715628423337003,"score_spread":0.3049949094013731,"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."}}