{"id":"W2995428961","doi":"10.1007/978-3-030-59065-9_24","title":"FIBS: A Generic Framework for Classifying Interval-Based Temporal Sequences","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Interval (graph theory); Set (abstract data type); Feature selection; Filter (signal processing); Feature (linguistics); Data mining; Artificial intelligence; Selection (genetic algorithm); Pattern recognition (psychology); Machine learning; Mathematics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007259832,0.0006172161,0.000813094,0.0005819669,0.0004303833,0.001057082,0.003720719,0.0003731165,0.00003031722],"category_scores_gemma":[0.000265458,0.0005381841,0.0004156277,0.001011995,0.0005957864,0.0005535599,0.001035183,0.0008216384,0.00002148746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002590407,"about_ca_system_score_gemma":0.0007298833,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002366908,"about_ca_topic_score_gemma":0.00005072102,"domain_scores_codex":[0.995752,0.00003503705,0.0007351321,0.00189848,0.0008422454,0.0007371149],"domain_scores_gemma":[0.9970549,0.0007803511,0.0005652284,0.001095658,0.0002572274,0.0002465726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001963018,0.00002587051,0.0001721325,0.000175536,0.00004697952,0.00009689828,0.0007493257,0.07367887,0.0001119971,0.07561343,0.00005548581,0.8492538],"study_design_scores_gemma":[0.0001326016,0.0002555013,0.00001370525,0.0005339782,0.00001627178,0.00001262294,4.630623e-7,0.8107162,0.000438363,0.1843441,0.002967509,0.0005687014],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002411455,0.0005153385,0.9939677,0.003144511,0.001196608,0.0003965443,0.00001204736,0.0002148064,0.0005282903],"genre_scores_gemma":[0.1289456,0.000009395952,0.867263,0.002934548,0.0007091031,0.00002101789,0.00001110657,0.00003981905,0.00006641194],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8486851,"threshold_uncertainty_score":0.9999799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04921335867930701,"score_gpt":0.2697120368243289,"score_spread":0.2204986781450219,"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."}}