{"id":"W1866189828","doi":"10.3968/j.ccc.1923670020120802.1689","title":"The Impact of Text-Messaging on Vocabulary Learning of Iranian EFL Learners","year":2012,"lang":"en","type":"article","venue":"Cross-cultural communication","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Session (web analytics); Class (philosophy); Vocabulary learning; Test (biology); Sentence; Computer science; Control (management); Mathematics education; Mobile phone; Psychology; Multimedia; Artificial intelligence; Linguistics; World Wide Web","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":[0.0007617435,0.0004226384,0.0003610491,0.0003683829,0.0004311368,0.0008642569,0.0003545629,0.0003850108,0.002876657],"category_scores_gemma":[0.00495565,0.00009174238,0.0003835886,0.0002167905,0.0002961262,0.0007089275,0.0005531051,0.000557846,0.0004757894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002339836,"about_ca_system_score_gemma":0.0004954025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000925459,"about_ca_topic_score_gemma":0.0008789038,"domain_scores_codex":[0.9994215,0.0001935371,0.00005080925,0.00006551827,0.0001697686,0.00009895956],"domain_scores_gemma":[0.9975788,0.001182023,0.0003710454,0.0000703718,0.0003402359,0.0004575271],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004117891,0.03089589,0.2098335,0.002145638,0.0002448993,0.001728318,0.04104853,0.0007433579,0.05360787,0.000687651,0.002973454,0.651973],"study_design_scores_gemma":[0.0006167407,0.05039778,0.8274975,0.000896816,0.001212226,0.001323136,0.05993751,0.002543211,0.03870178,0.001802662,0.01491034,0.0001602589],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998219,0.0001963317,0.00005677166,0.00008840178,0.00001274153,0.00001944005,0.00001342344,0.000007238621,0.001386717],"genre_scores_gemma":[0.9979026,0.0003701909,0.0004388431,0.0000651751,0.00001844598,0.00003328361,0.00003346101,0.000003677243,0.001134415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002876657,"threshold_uncertainty_score":0.009623408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364329137205363,"score_gpt":0.3553266061585318,"score_spread":0.3316833147864782,"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."}}