{"id":"W7067549303","doi":"","title":"Miten tavallisuudesta poikkeava mainoskampanja tavoittaa paikallislehden tilaajan? : huomioarvotutkimus Kauhavan kaupungin mainoskampanjasta","year":2018,"lang":"fi","type":"other","venue":"Theseus (Ammattikorkeakoulujen)","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Work (physics); Subject (documents); Data collection; Government (linguistics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002013912,0.0004935699,0.0004188388,0.0006736745,0.006331285,0.01405315,0.001016281,0.002712145,0.1035192],"category_scores_gemma":[0.004115685,0.0004048412,0.0004988734,0.0009729383,0.00311797,0.006633215,0.006498442,0.003761443,0.03365478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004924655,"about_ca_system_score_gemma":0.007765976,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01658125,"about_ca_topic_score_gemma":0.04867265,"domain_scores_codex":[0.9977901,0.0005025317,0.00009304505,0.0004026368,0.0006981518,0.0005136361],"domain_scores_gemma":[0.9973289,0.0006055289,0.0001938111,0.0002033041,0.000848951,0.0008194564],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0003105226,0.0002660746,0.01002014,0.001053317,0.00003786357,0.001527604,0.04097878,0.0001832873,0.006402696,0.1857792,0.4923097,0.2611308],"study_design_scores_gemma":[0.000004131365,0.00002370825,0.00190894,0.0001938493,0.000007766243,0.0001723053,0.009045482,0.00005230043,0.0007131394,0.003148043,0.9847136,0.0000167248],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03927204,0.01192755,0.00652102,0.09404793,0.009415237,0.0001751618,0.0008981453,0.0008344119,0.8369085],"genre_scores_gemma":[0.1123687,0.007170233,0.005880981,0.009935786,0.001057782,0.0001295643,0.0005298189,0.0005976967,0.8623294],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1035192,"threshold_uncertainty_score":0.3463061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01065050966892167,"score_gpt":0.2541382004429231,"score_spread":0.2434876907740014,"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."}}