{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001435126,0.00006683137,0.00005037724,0.00002916018,0.0008007928,0.0004087063,0.0003764697,0.00003191673,0.00003253499],"category_scores_gemma":[0.00004734248,0.00004866429,0.00002751256,0.00004676032,0.00004379164,0.00035432,0.00006830418,0.0001053884,0.000339782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002379631,"about_ca_system_score_gemma":0.00002412032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007485938,"about_ca_topic_score_gemma":0.00007151046,"domain_scores_codex":[0.9994583,0.00002335535,0.00008649869,0.000170577,0.0001336753,0.0001276287],"domain_scores_gemma":[0.9993279,0.00003690936,0.00007870129,0.0004545343,0.00006536108,0.00003659612],"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":[0.000001897097,0.00001562675,0.00008968046,6.927804e-7,0.000004999879,0.000001557561,0.00003330258,0.00004167651,0.0000418329,0.05325826,0.003068196,0.9434423],"study_design_scores_gemma":[0.0006141738,0.0002678961,0.05880152,0.00001543545,0.000005048019,0.00002438955,0.000006176174,0.6915416,0.0005710827,0.005870671,0.2420658,0.0002162086],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0002987563,0.00002447084,0.9160784,0.0391693,0.001092395,0.0001426654,0.000001045696,0.0001790967,0.04301379],"genre_scores_gemma":[0.9650745,0.0003913687,0.02383989,0.001579621,0.0004048925,0.00002791669,0.000003328559,0.00001513156,0.008663399],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9647757,"threshold_uncertainty_score":0.6159132,"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."}}