{"id":"W2368869429","doi":"","title":"Forestry information text classification algorithm based on GMM model","year":2014,"lang":"en","type":"article","venue":"Zhongnan Linye Keji Daxue xuebao","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"Mixture model; Computer science; Artificial intelligence; Decision tree; Naive Bayes classifier; Gaussian; Classifier (UML); Pattern recognition (psychology); tf–idf; Statistical classification; Algorithm; Data mining; Machine learning; Support vector machine","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004213677,0.0002738722,0.0002198905,0.000431069,0.0002728399,0.000504463,0.001424704,0.0002495673,0.00001784986],"category_scores_gemma":[0.0002184891,0.0002517917,0.0001078961,0.0006207338,0.0001032159,0.002062242,0.0001625531,0.0003087645,0.0007655836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001149061,"about_ca_system_score_gemma":0.0001233196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053078,"about_ca_topic_score_gemma":0.000002152332,"domain_scores_codex":[0.9979874,0.00006351261,0.0005111893,0.0004706878,0.0005608342,0.0004063866],"domain_scores_gemma":[0.9977286,0.0001391571,0.000326524,0.001518215,0.0001653115,0.0001221755],"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":[0.000009644291,0.0001214379,0.0003042645,0.00002482951,0.000007684991,6.258932e-7,0.000176324,0.01189788,0.0002526255,0.3202541,0.008940896,0.6580096],"study_design_scores_gemma":[0.000516465,0.0001117158,0.002053944,0.0000326076,0.000005745479,0.000001442134,0.0000503756,0.9494975,0.002166349,0.01527329,0.0299946,0.000295923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001856982,0.0000121248,0.9773504,0.004608778,0.0003263124,0.0003066447,0.00001181996,0.001255696,0.01427124],"genre_scores_gemma":[0.8495889,0.00001001211,0.1478489,0.001362563,0.00008496422,0.0001235862,0.000090839,0.00001773458,0.0008724419],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9375997,"threshold_uncertainty_score":0.9999934,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571889091668318,"score_gpt":0.2335009435446938,"score_spread":0.2177820526280106,"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."}}