{"id":"W2009955015","doi":"10.1145/1569901.1596274","title":"Design &amp; Implementation of Parallel Linear GP for the IBM Cell Processor","year":2009,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"Brock University","keywords":"Operand; Computer science; Parallel computing; SIMD; Population; IBM; Selection (genetic algorithm); Algorithm; Artificial intelligence; Computer hardware","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":[0.0001532371,0.00004770127,0.00004688347,0.00001725877,0.0001247791,0.00001712898,0.0003499357,0.0000149348,0.00001715077],"category_scores_gemma":[0.000002944289,0.00003145925,0.00002902255,0.0001572382,0.00001174214,0.0001428744,0.00001982703,0.00002317889,0.00001100931],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007145576,"about_ca_system_score_gemma":0.00005403653,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001388595,"about_ca_topic_score_gemma":0.000004724917,"domain_scores_codex":[0.9995305,0.000009900135,0.0001375364,0.0001273277,0.00009346541,0.000101298],"domain_scores_gemma":[0.9995105,0.0001008584,0.00006244602,0.0002147004,0.00009176767,0.00001978645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005768031,0.000936858,0.0001661228,0.00005345799,0.00004041121,2.712545e-7,0.00323223,0.04578804,0.0164293,0.6068429,0.07105102,0.2554017],"study_design_scores_gemma":[0.002657644,0.0004861251,0.006846654,0.00000575446,0.00002787319,0.000004820362,0.0002741257,0.8336141,0.0329899,0.07950342,0.04328198,0.0003075902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003798897,0.00006316372,0.9940089,0.004624533,0.00001879629,0.0006108657,0.000001962422,0.00003753169,0.0002543743],"genre_scores_gemma":[0.1418454,0.00001880791,0.856733,0.0003207001,0.00003907644,0.000141948,0.00000466014,0.000002079586,0.0008943563],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7878261,"threshold_uncertainty_score":0.1282871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0421437294131212,"score_gpt":0.3239630175252129,"score_spread":0.2818192881120917,"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."}}