{"id":"W2943818116","doi":"10.5539/mas.v13n5p88","title":"Arabic Text Classification: A Review","year":2019,"lang":"en","type":"review","venue":"Modern Applied Science","topic":"Text and Document Classification Technologies","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Arabic; Weighting; Artificial intelligence; Classifier (UML); Decision tree; Natural language processing; Naive Bayes classifier; Support vector machine; Set (abstract data type); Data mining; Information retrieval; Machine learning; Linguistics","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":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001259743,0.0005413587,0.001443191,0.0005965381,0.0003527761,0.0006251535,0.009007856,0.0002532192,0.00003727962],"category_scores_gemma":[0.0001117071,0.0004152638,0.0002965436,0.003893566,0.0006560829,0.0007572682,0.001295435,0.0005560976,0.003685707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003545083,"about_ca_system_score_gemma":0.00148235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":7.160332e-7,"about_ca_topic_score_gemma":2.243566e-7,"domain_scores_codex":[0.9952954,0.00004940388,0.0008738509,0.001936239,0.001167787,0.0006772854],"domain_scores_gemma":[0.9947302,0.0001401127,0.0008177595,0.004012514,0.0001440819,0.0001553113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[1.193034e-7,0.0000204663,7.418936e-8,0.005872312,0.000005479724,6.149835e-7,0.0000155487,1.091536e-7,0.00002830046,0.1398639,0.001461939,0.8527312],"study_design_scores_gemma":[0.00005144958,0.00001249527,0.000002098273,0.006011493,0.00007633518,0.00001760396,0.000003625637,0.002596942,0.000006325237,0.0032284,0.9874792,0.0005140384],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[1.035126e-8,0.7031172,0.2794556,0.0004388326,0.0002496741,0.001243543,0.000002915918,0.0006487193,0.01484354],"genre_scores_gemma":[0.00004719185,0.9900385,0.006960151,0.0004230099,0.00003492555,0.0006795048,0.00001141839,0.00002633916,0.001778949],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9860172,"threshold_uncertainty_score":0.9998299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.126081744370099,"score_gpt":0.3611282266457068,"score_spread":0.2350464822756078,"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."}}