{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005241176,0.0002218694,0.0003296108,0.0001352754,0.000500674,0.0004409419,0.0001607937,0.00006341486,0.000006311838],"category_scores_gemma":[0.0006787058,0.0002204122,0.00006436618,0.0004565488,0.0001045077,0.0007399016,0.0001961282,0.0001344494,0.000001470097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004089203,"about_ca_system_score_gemma":0.0001849105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007687927,"about_ca_topic_score_gemma":0.0001607239,"domain_scores_codex":[0.9982641,0.00008243843,0.0004487715,0.0006093711,0.0002347891,0.0003605028],"domain_scores_gemma":[0.9987041,0.000260484,0.0001615067,0.0002686046,0.0004222308,0.0001830249],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003884529,0.0005246119,0.05119274,0.002170751,0.0007673171,0.00006718241,0.004685327,0.3122927,0.3369023,0.1480746,0.001710356,0.1412237],"study_design_scores_gemma":[0.0002947022,0.0001026392,0.000165862,0.00007485293,0.00007819433,0.00004309085,0.00005207419,0.9858932,0.00733633,0.005442784,0.0002573949,0.0002588764],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1004112,0.0005092957,0.8974231,0.001098376,0.00005189539,0.000240041,0.0000485797,0.0001401516,0.00007736655],"genre_scores_gemma":[0.3350836,0.0000103268,0.6642786,0.0002229464,0.00009745399,0.00002405934,0.00009139427,0.00002263134,0.0001689185],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6736005,"threshold_uncertainty_score":0.8988147,"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."}}