{"id":"W2100398949","doi":"","title":"Prediction and change detection in sequential data for interactive applications","year":2008,"lang":"en","type":"article","venue":"Griffith Research Online (Griffith University, Queensland, Australia)","topic":"Data Stream Mining Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Australian Government","keywords":"Computer science; Machine learning; Change detection; Artificial intelligence; Support vector machine; Data mining; Time series","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008976339,0.0002255238,0.0002668908,0.001004678,0.0004901849,0.0001074946,0.001949256,0.0002162676,0.000006947213],"category_scores_gemma":[0.0001359486,0.0002462023,0.00004475676,0.001376674,0.0003396447,0.002164255,0.001758335,0.000755057,0.00001131076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002846019,"about_ca_system_score_gemma":0.0001654545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00352679,"about_ca_topic_score_gemma":0.006503911,"domain_scores_codex":[0.9971443,0.0003075403,0.0002694526,0.001080073,0.0005572441,0.0006413994],"domain_scores_gemma":[0.9975854,0.0003310351,0.0001233727,0.001342836,0.0003953625,0.0002220434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00465135,0.008406033,0.2544752,0.001471937,0.001013039,0.002350898,0.01808745,0.0001138834,0.01371941,0.0295223,0.1085719,0.5576166],"study_design_scores_gemma":[0.007563554,0.002701995,0.6666119,0.0004982195,0.00009726985,0.000679491,0.002027403,0.06234554,0.003461709,0.002767615,0.2496819,0.001563438],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6075814,0.00009820105,0.3742278,0.003402492,0.000356749,0.00505829,0.008116464,0.0008924427,0.0002661663],"genre_scores_gemma":[0.9414956,0.000648401,0.05443604,0.00003451066,0.0004480858,0.00008770598,0.001813561,0.000027681,0.001008439],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5560532,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4241822169354496,"score_gpt":0.4213390490140103,"score_spread":0.002843167921439238,"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."}}