{"id":"W2386170285","doi":"","title":"Chinese Text Categorization Technology Using BP Neural Network","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Computational Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Categorization; Artificial intelligence; Artificial neural network; Macro; Text categorization; Cross entropy; Classifier (UML); Entropy (arrow of time); Machine learning; Test set; Dimension (graph theory); Data mining; Natural language processing; Pattern recognition (psychology); Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006168816,0.0002301937,0.0002009015,0.0002174548,0.0007919315,0.00006968244,0.001140969,0.0001113021,0.000005048633],"category_scores_gemma":[7.221664e-7,0.0002311661,0.00008098349,0.002370812,0.000116564,0.0003769377,0.0004331031,0.0001995501,0.00006899243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007893633,"about_ca_system_score_gemma":0.00007930078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006222765,"about_ca_topic_score_gemma":0.000001292913,"domain_scores_codex":[0.9984596,0.00002304401,0.0003718595,0.0006128837,0.0001646822,0.0003679247],"domain_scores_gemma":[0.9987788,0.00005535972,0.0001745108,0.0006806134,0.0002161494,0.00009454888],"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":[0.000003167115,0.000323481,0.0038795,0.0000174359,0.00003957393,0.00001176476,0.0002376053,0.2020929,0.01229001,0.5390626,0.003915629,0.2381263],"study_design_scores_gemma":[0.0003039978,0.00003617271,0.002524306,0.000009390674,0.000008952525,0.0006789492,0.000003158767,0.5682095,0.001070487,0.1976378,0.2289724,0.0005449378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006885991,0.000222954,0.9892799,0.001449079,0.0000301799,0.00074128,0.000004199559,0.001056773,0.0003296457],"genre_scores_gemma":[0.229667,0.00002182198,0.7687715,0.0006718445,0.0002824827,0.0004553417,0.00003098816,0.00002084415,0.00007824317],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3661166,"threshold_uncertainty_score":0.9426678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01376611834189483,"score_gpt":0.2808374720178594,"score_spread":0.2670713536759646,"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."}}