{"id":"W1997857295","doi":"10.1145/2742854.2742883","title":"Genesis","year":2015,"lang":"en","type":"article","venue":"","topic":"Parallel Computing and Optimization Techniques","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Qualcomm","keywords":"Computer science; Preprocessor; Language model; Code (set theory); Artificial intelligence; Component (thermodynamics); Natural language processing; Range (aeronautics); Machine learning; Programming language; Set (abstract data type)","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":[0.000121966,0.00002905253,0.00003236307,0.00003091953,0.00001834816,0.0000471386,0.0003169579,0.00001358422,0.000003700249],"category_scores_gemma":[0.00001599353,0.00002405354,0.00001107524,0.0001347112,0.000004940142,0.0001157944,0.00009524154,0.00001464906,0.00008327665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008320927,"about_ca_system_score_gemma":0.00002664065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001557345,"about_ca_topic_score_gemma":2.887595e-7,"domain_scores_codex":[0.9996988,0.00001448563,0.00004981965,0.00009136741,0.00007976514,0.00006573489],"domain_scores_gemma":[0.9996774,0.000007884743,0.00001197165,0.000192444,0.00005224181,0.0000580635],"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":[0.000001510002,0.00005128287,0.001571929,0.000001795033,0.00000650958,0.000007985638,0.0006224456,0.009211053,0.00005359534,0.5436134,0.3628249,0.08203357],"study_design_scores_gemma":[0.0001608359,0.00004974933,0.0003389434,0.000001992438,7.165946e-7,0.00001261092,0.00001153,0.9280851,0.00466943,0.0186848,0.04783499,0.0001492577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003951074,0.00004103683,0.924725,0.0004683154,0.00006929416,0.00001695732,1.915516e-8,0.0007836759,0.07350059],"genre_scores_gemma":[0.1793909,0.000002561004,0.8185785,0.0004768418,0.00001751087,0.000001588531,1.766625e-7,0.000001552828,0.001530306],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9188741,"threshold_uncertainty_score":0.1070381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04207017513109721,"score_gpt":0.2738546018180721,"score_spread":0.2317844266869749,"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."}}