{"id":"W2071505781","doi":"10.1007/s10766-013-0245-x","title":"Parallel Training of An Improved Neural Network for Text Categorization","year":2013,"lang":"en","type":"article","venue":"International Journal of Parallel Programming","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Francis Xavier University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Speedup; Computer science; Categorization; Correctness; Artificial neural network; The Internet; Artificial intelligence; Text categorization; Process (computing); Machine learning; Data mining; Parallel computing; Algorithm; World Wide Web","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001015685,0.0007554996,0.001094236,0.0008120319,0.0005813543,0.0007993101,0.001674694,0.001010692,0.005425381],"category_scores_gemma":[0.002530736,0.0004527317,0.0006641573,0.001178867,0.000341127,0.00148392,0.0009293712,0.001356952,0.001729221],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007737922,"about_ca_system_score_gemma":0.001293055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009839626,"about_ca_topic_score_gemma":0.01225385,"domain_scores_codex":[0.9994557,0.00008257048,0.00004302936,0.0001934421,0.0001370457,0.00008821864],"domain_scores_gemma":[0.9988891,0.0003077669,0.0000509446,0.0002356092,0.0004592554,0.00005720638],"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":[0.0005583989,0.0003792682,0.001315828,0.0001053078,0.0001231807,0.000114701,0.00007068392,0.1608001,0.01927485,0.002141251,0.006957792,0.8081587],"study_design_scores_gemma":[0.00002169003,0.00006054752,0.0002765729,0.000003557551,0.00002362759,0.00002778012,0.0000101591,0.9933503,0.00406111,0.001379783,0.0007802737,0.000004639611],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1332708,0.001018519,0.8527572,0.0005303498,0.0007086726,0.0001860309,0.0003267076,0.005658534,0.005543217],"genre_scores_gemma":[0.5851277,0.0003422251,0.3983788,0.0003130157,0.0002691073,0.0002477127,0.001085424,0.0002655821,0.01397034],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009839626,"threshold_uncertainty_score":0.01956469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03387168784162624,"score_gpt":0.2973515778122583,"score_spread":0.263479889970632,"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."}}