{"id":"W4286212785","doi":"10.5539/ijel.v12n4p106","title":"Evaluating Telegram Application to Empower the Students’ Vocabulary Mastery","year":2022,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"English Language Learning and Teaching","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Mathematics education; Test (biology); Cluster sampling; Population; Psychology; Class (philosophy); Computer science; Artificial intelligence; Sociology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002577756,0.0001039006,0.0001252719,0.0001960581,0.0002713225,0.0003450522,0.002970011,0.00002269795,0.00002871239],"category_scores_gemma":[0.03637823,0.00008493423,0.00009705006,0.000224118,0.00001595437,0.00008973374,0.0008737827,0.0007415301,0.00000540445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001697904,"about_ca_system_score_gemma":0.00009860744,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001232065,"about_ca_topic_score_gemma":0.000001492296,"domain_scores_codex":[0.9972835,0.0002530043,0.0004695546,0.0001793943,0.001655468,0.0001590354],"domain_scores_gemma":[0.9926658,0.0004063511,0.0004200498,0.0003084585,0.006118712,0.00008066708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002575922,0.001269206,0.04742226,0.00002118162,0.0009299622,0.0006825413,0.1138368,0.5038195,0.0003367861,0.07728805,0.03147538,0.2226608],"study_design_scores_gemma":[0.0008877991,0.0005341589,0.002880599,0.00004703236,0.00003902592,0.00004708938,0.003738869,0.02032493,0.0001227037,0.001563833,0.9695334,0.0002804988],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3202713,0.0006115608,0.584787,0.001022612,0.07282566,0.0004730881,0.0000181472,0.0002314439,0.0197592],"genre_scores_gemma":[0.9701482,0.000002896269,0.02086639,0.001127934,0.007665523,0.00001304899,0.000004228408,0.00001414199,0.0001576266],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9380581,"threshold_uncertainty_score":0.9717388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01910874174158475,"score_gpt":0.3450315110438435,"score_spread":0.3259227693022587,"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."}}