{"id":"W2911083821","doi":"10.5539/mas.v13n2p95","title":"The Design and the Construction of the Traditional Arabic Lexicons Corpus (The TAL-Corpus)","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Edcuational Technology Systems","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Natural language processing; Lexicography; Corpus linguistics; Root (linguistics); Lexicographical order; Artificial intelligence; Arabic; Vocabulary; Word (group theory); Text corpus; Lexicon; Lexical database; Modern Standard Arabic; Linguistics; WordNet; 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":["sts"],"consensus_categories":[],"category_scores_codex":[0.00252055,0.0001126487,0.0001261936,0.00005079411,0.001151502,0.000199968,0.00263128,0.00005060682,0.000002068444],"category_scores_gemma":[0.00008533433,0.00004727953,0.00004533416,0.0007462057,0.005858631,0.0002182037,0.0003435401,0.0002305849,0.00002145588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004837108,"about_ca_system_score_gemma":0.0002795643,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001215445,"about_ca_topic_score_gemma":0.00000566164,"domain_scores_codex":[0.9982164,0.0001259237,0.0002452628,0.0003875363,0.0007780088,0.0002469069],"domain_scores_gemma":[0.997594,0.001067578,0.0002484109,0.0009602841,0.00009939029,0.00003031303],"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":[0.000008751793,0.000007539841,0.0002042986,0.000001808441,0.000005753075,7.667202e-8,0.0004144739,0.0009380627,0.03157448,0.9603145,0.00003452156,0.00649579],"study_design_scores_gemma":[0.0003985073,0.00001607602,0.005331983,0.000009567004,0.00000559712,0.00009478509,0.0001125981,0.5250725,0.01027591,0.4583706,0.0002263895,0.00008547647],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1525371,0.0001781895,0.8334149,0.008939138,0.0008293641,0.001329995,0.00000278883,0.00007957686,0.00268894],"genre_scores_gemma":[0.9954893,0.000009629694,0.00402431,0.0002496116,0.00002370901,0.00009243999,1.499065e-7,0.000004122111,0.0001067311],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8429522,"threshold_uncertainty_score":0.9968469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936480260259765,"score_gpt":0.1996712384491961,"score_spread":0.1803064358465985,"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."}}