{"id":"W2398436144","doi":"10.1016/j.artint.2016.05.004","title":"Automatic construction of parallel portfolios via algorithm configuration","year":2016,"lang":"en","type":"article","venue":"Artificial Intelligence","topic":"Constraint Satisfaction and Optimization","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Algorithm; Parallel computing","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":[0.0001764596,0.00009694565,0.0001253648,0.0001265698,0.00007147615,0.00004186545,0.0002189339,0.00005740469,0.0005347106],"category_scores_gemma":[0.00007082419,0.00007607998,0.00005116789,0.0003071888,0.0001544046,0.0005380462,0.00003316223,0.00004185382,0.0001753795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003168948,"about_ca_system_score_gemma":0.00006494566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002498911,"about_ca_topic_score_gemma":0.00001101693,"domain_scores_codex":[0.998897,0.00005016688,0.0004627858,0.0002337198,0.0002081013,0.0001482409],"domain_scores_gemma":[0.9991736,0.0001010885,0.000210746,0.0002740048,0.0001816275,0.00005894968],"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":[0.000001721835,0.00001688866,0.00009619377,0.00000247149,0.000005661672,0.000001197995,0.00009896601,0.0000891229,0.00379683,0.16316,0.00001196369,0.832719],"study_design_scores_gemma":[0.00005140491,0.00008111536,0.0009669988,0.00004298907,0.000007763975,0.00004492465,0.00009446415,0.7163196,0.1825424,0.09952412,0.0001290268,0.0001952904],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003942852,0.000009519394,0.9937965,0.0008781057,0.0004871576,0.0001496232,0.000002527665,0.0001418446,0.0005918191],"genre_scores_gemma":[0.7879332,0.00002488279,0.2119132,0.00004930202,0.00003335359,0.000008621793,0.00000160187,0.000004152127,0.0000317291],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8325236,"threshold_uncertainty_score":0.5854707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02348581171794199,"score_gpt":0.2612301690845495,"score_spread":0.2377443573666075,"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."}}