{"id":"W4254352688","doi":"10.5194/acp-2017-1126-rc3","title":"Interactive comment on “Associativity Analysis of SO2 and NO2 for Alberta Monitoring Data Using KZ Filtering and Hierarchical Clustering\"","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Associative property; Cluster analysis; Hierarchical clustering; Computer science; Data mining; Mathematics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005971767,0.001049046,0.001012286,0.001775257,0.003483608,0.004618685,0.002566104,0.007314526,0.1864531],"category_scores_gemma":[0.05113697,0.0006876548,0.001532972,0.002419376,0.001626743,0.002187846,0.002775436,0.004356432,0.04304356],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006614854,"about_ca_system_score_gemma":0.007984201,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2891673,"about_ca_topic_score_gemma":0.2744464,"domain_scores_codex":[0.9958149,0.0006230986,0.0002183481,0.0004062085,0.002419604,0.000517963],"domain_scores_gemma":[0.9692053,0.009228916,0.0009308105,0.001973338,0.01741155,0.001250261],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004037143,0.0000043953,0.000144165,0.00003008812,0.000007531376,0.00002936689,0.00005670234,0.0001418018,0.0001044005,0.0009866225,0.9973463,0.001108219],"study_design_scores_gemma":[0.00009234766,0.00001012811,0.004810554,0.0001161219,0.00002038964,0.00004068774,0.0002708666,0.001759069,0.0009554201,0.004079261,0.9877657,0.00007945135],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.004375829,0.0009327523,0.007399533,0.5968622,0.2404736,0.0003130707,0.08203766,0.005447768,0.06215764],"genre_scores_gemma":[0.1028516,0.001588101,0.01807668,0.2047893,0.06500568,0.0006852151,0.02704258,0.007721035,0.5722398],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.7108327,"threshold_uncertainty_score":0.6237477,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1838563083408931,"score_gpt":0.3894247410584974,"score_spread":0.2055684327176043,"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."}}