{"id":"W2184927432","doi":"","title":"MEASURING DEPENDENCE VIA MUTUAL INFORMATION","year":2011,"lang":"en","type":"dissertation","venue":"QSpace (Queen's University Library)","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University","keywords":"Mutual information; Computer science; Information retrieval; Data science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00003477233,0.0002838862,0.0002257158,0.0003303846,0.0003314836,0.0002327549,0.001665581,0.0002673326,0.0001063418],"category_scores_gemma":[0.000005944682,0.0003256908,0.0001359761,0.0006995576,0.000029145,0.007053455,0.0003053636,0.0004408123,0.0003966355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000492837,"about_ca_system_score_gemma":0.0001988488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001283483,"about_ca_topic_score_gemma":0.00006948703,"domain_scores_codex":[0.9986822,0.00005903949,0.0001871801,0.0004099788,0.0003331419,0.0003284586],"domain_scores_gemma":[0.9987299,0.00004547924,0.0003070752,0.0006439339,0.00008513009,0.0001885054],"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":[0.0003284743,0.000320624,0.002309526,0.0005518708,0.0002873721,0.0002586893,0.008552999,0.0005123041,0.00007456718,0.399977,0.4730306,0.113796],"study_design_scores_gemma":[0.001133162,0.0002705619,0.02386246,0.0005453008,0.0001802645,0.00000525501,0.001697742,0.002640416,0.01716299,0.01020339,0.9392464,0.003052114],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06355432,0.0001248407,0.5100451,0.02924277,0.003811872,0.00293044,0.0001066787,0.005265214,0.3849188],"genre_scores_gemma":[0.5638897,0.0008010104,0.0472499,0.0006969444,0.0003286948,0.00001425151,0.001493839,0.0000896647,0.385436],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.5003354,"threshold_uncertainty_score":0.9999195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01114214888018337,"score_gpt":0.1758898782541602,"score_spread":0.1647477293739768,"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."}}