{"id":"W3195755830","doi":"10.15446/ing.investig.v41n3.79308","title":"Full Model Selection Problem and Pipelines for Time-Series Databases: Contrasting Population-Based and Single-point Search Metaheuristics","year":2021,"lang":"en","type":"article","venue":"Ingeniería e Investigación","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Metaheuristic; Computer science; Pipeline (software); Population; Data mining; Pipeline transport; Artificial intelligence; Engineering","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":[0.002268915,0.0009564871,0.001434142,0.001115432,0.0004000735,0.00115659,0.00171452,0.001694943,0.001031247],"category_scores_gemma":[0.004113747,0.0006982047,0.001144384,0.0012747,0.0007176245,0.001822529,0.0009275392,0.001600706,0.0001561877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001092867,"about_ca_system_score_gemma":0.001627768,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007835033,"about_ca_topic_score_gemma":0.004146454,"domain_scores_codex":[0.999442,0.0002111062,0.00003619607,0.0001178855,0.0001252507,0.0000675511],"domain_scores_gemma":[0.9982952,0.001251944,0.0001264621,0.00009675844,0.0001601318,0.00006938663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004578186,0.00004306541,0.0005479479,0.00003984306,0.00004402093,0.00002539556,0.00002889522,0.9666647,0.000502088,0.003960496,0.0003018426,0.02779595],"study_design_scores_gemma":[0.000006342248,0.0000160496,0.00006994263,0.000002643756,0.000004706349,0.000006676292,0.000005351716,0.9984131,0.0001742869,0.001149039,0.0001497195,0.00000212853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04308286,0.0008830347,0.9536794,0.0004404873,0.00003026562,0.00007610395,0.00006304048,0.0003033331,0.001441431],"genre_scores_gemma":[0.6067023,0.0009290478,0.3888066,0.0002685706,0.00005996522,0.0003892943,0.0002758785,0.000117316,0.002451101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007835033,"threshold_uncertainty_score":0.01557887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890749182613813,"score_gpt":0.2539276825346602,"score_spread":0.2150201907085221,"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."}}