{"id":"W2783872167","doi":"","title":"Classification algorithms and how to distribute them","year":2017,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"IBM (Canada); Queen's University","funders":"","keywords":"Computer science; Rewriting; Algorithm; Statistical classification; IBM; Classifier (UML); Categorization; Big data; Plug-in; SPARK (programming language); Machine learning; Artificial intelligence; Data mining; Programming language","routes":{"ca_aff":true,"ca_fund":false,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0007186435,0.000110336,0.00009936124,0.00009083514,0.0007135949,0.002119224,0.0010965,0.00002925246,3.288075e-7],"category_scores_gemma":[0.0006466269,0.0001001447,0.00001156992,0.0001904936,0.0001083846,0.001336425,0.0007457703,0.0001023544,0.000006825473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002897774,"about_ca_system_score_gemma":0.00004030369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006485401,"about_ca_topic_score_gemma":5.536542e-7,"domain_scores_codex":[0.998911,0.00001004327,0.00007560255,0.0004988547,0.0002689656,0.0002355056],"domain_scores_gemma":[0.9987542,0.00007088174,0.00005964137,0.0008029485,0.0001121066,0.0002002083],"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":[6.155846e-7,0.000008289576,0.01435087,0.00001855862,0.000003246075,0.000003724691,0.0003537872,0.0001720171,0.0006993024,0.01801394,0.0003274218,0.9660482],"study_design_scores_gemma":[0.00006401632,0.00002057444,0.3678279,0.00001897521,0.000001252583,0.00001690786,0.000003078494,0.6252393,0.0001150396,0.00005919365,0.006508594,0.0001251348],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02657273,0.0000272386,0.9693784,0.003381819,0.0003515103,0.00007542771,0.000002637802,0.0001941197,0.00001612997],"genre_scores_gemma":[0.6534959,0.0000112015,0.3463012,0.00007251595,0.00008294556,0.000006702823,0.000002221883,0.000003890271,0.00002337653],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9659231,"threshold_uncertainty_score":0.9989167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636521978523449,"score_gpt":0.2499284185734242,"score_spread":0.2235631987881897,"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."}}