{"id":"W2945844905","doi":"10.5539/mas.v13n6p24","title":"Enhanced Arabic Information Retrieval by Using Arabic Slang Language","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Translation Studies and Practices","field":"Arts and Humanities","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Slang; Computer science; Arabic; Linguistics; Natural language processing; Context (archaeology); Grammar; Artificial intelligence; History","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009185978,0.00085747,0.0006601643,0.002972837,0.0006154448,0.001876442,0.0005081376,0.0005750662,0.006368985],"category_scores_gemma":[0.003474468,0.0001840309,0.0005430472,0.001998908,0.0002716271,0.003234785,0.00114098,0.0004442099,0.005403647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000490681,"about_ca_system_score_gemma":0.000750663,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003390689,"about_ca_topic_score_gemma":0.002492393,"domain_scores_codex":[0.9990037,0.0002986646,0.0001380826,0.0001261742,0.0003519529,0.00008154081],"domain_scores_gemma":[0.99862,0.0003299271,0.00009392839,0.0001716668,0.0007328892,0.00005162306],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007841044,0.0003276818,0.001857358,0.001429171,0.00007378051,0.0008819593,0.001507101,0.003621221,0.1102036,0.004923428,0.04164775,0.8327429],"study_design_scores_gemma":[0.0003618528,0.0008946647,0.007737172,0.00035591,0.0004160558,0.003621123,0.003358077,0.257877,0.3097489,0.009189054,0.4059715,0.0004686785],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.248765,0.006375127,0.6188395,0.003465279,0.0009112678,0.001756984,0.005162688,0.05893571,0.05578853],"genre_scores_gemma":[0.3609403,0.001971279,0.6062778,0.0009581242,0.0003141794,0.0003331883,0.005644209,0.0007813034,0.02277959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006368985,"threshold_uncertainty_score":0.0213064,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02160509935217118,"score_gpt":0.253420534747436,"score_spread":0.2318154353952648,"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."}}