{"id":"W2267545879","doi":"10.1609/aimag.v38i2.2722","title":"The ICON Challenge on Algorithm Selection","year":2017,"lang":"en","type":"article","venue":"AI Magazine","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"University of British Columbia","keywords":"Icon; Selection (genetic algorithm); Variety (cybernetics); Relevance (law); Computer science; Machine learning; Algorithm; Selection algorithm; Artificial intelligence; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03359307,0.002475172,0.003842356,0.003090659,0.002333639,0.008981103,0.005722053,0.008586942,0.01588292],"category_scores_gemma":[0.1175521,0.000755181,0.002877266,0.005415754,0.00364803,0.008339768,0.005498996,0.008593794,0.006968908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004270412,"about_ca_system_score_gemma":0.008317454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006486813,"about_ca_topic_score_gemma":0.007579468,"domain_scores_codex":[0.9495954,0.02253967,0.003370644,0.005595958,0.01719465,0.001703711],"domain_scores_gemma":[0.8239064,0.1353376,0.002034992,0.01438059,0.02102169,0.003318868],"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":[0.000479004,0.0002610259,0.001134627,0.002174512,0.0002548503,0.0001474668,0.0001594866,0.03787912,0.001626552,0.06559765,0.4261514,0.4641343],"study_design_scores_gemma":[0.0005507756,0.0004959654,0.00199091,0.001178458,0.0001648904,0.0006848181,0.0004325375,0.2629506,0.007035295,0.2467313,0.4775718,0.0002125678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01760201,0.08045942,0.7005551,0.0831905,0.01572659,0.001266487,0.004870048,0.00565918,0.09067061],"genre_scores_gemma":[0.1424202,0.02436659,0.7282268,0.03021176,0.01370833,0.001542171,0.0196216,0.00578538,0.03411703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03359307,"threshold_uncertainty_score":0.1776593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0139382738647652,"score_gpt":0.2670835358696214,"score_spread":0.2531452620048562,"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."}}