{"id":"W3043675992","doi":"10.5539/ijel.v10n5p203","title":"Improving Speaker’s Use of Segmental and Suprasegmental Features of L2 Speech","year":2020,"lang":"en","type":"article","venue":"International Journal of English Linguistics","topic":"EFL/ESL Teaching and Learning","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pronunciation; Psychology; Phonology; Diphthong; Intonation (linguistics); Linguistics; Articulation (sociology); Second-language acquisition; Phonetics; Reflection (computer programming); Stress (linguistics); Computer science; Vowel","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":[0.0005598413,0.0003538017,0.0002053941,0.0002623904,0.0002091334,0.0006305099,0.0002716183,0.0002911793,0.002994567],"category_scores_gemma":[0.00237725,0.00007263949,0.0001973724,0.0001285225,0.0001694343,0.0003547739,0.0006865161,0.0003283416,0.0005187552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001902266,"about_ca_system_score_gemma":0.0003716651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001602724,"about_ca_topic_score_gemma":0.001894528,"domain_scores_codex":[0.9995914,0.0001343771,0.00003155316,0.00008343395,0.0001037618,0.00005538871],"domain_scores_gemma":[0.9988253,0.0004750082,0.0002166743,0.000103344,0.0002322989,0.0001473786],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0009564525,0.004631248,0.06360769,0.0007061161,0.00006956406,0.0004416108,0.01829925,0.0007880998,0.5156707,0.0002692532,0.0006924368,0.3938676],"study_design_scores_gemma":[0.0001282303,0.01008766,0.6664559,0.0001951883,0.0002748379,0.001317878,0.01799916,0.003680843,0.2879764,0.0003519846,0.01143289,0.00009894033],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983463,0.00004100738,0.0006173074,0.00002475722,0.000002308519,0.00001450776,0.00001427157,0.00002321989,0.0009162384],"genre_scores_gemma":[0.9950446,0.00009488471,0.003203048,0.00002696609,0.000003496752,0.00002796867,0.00004254054,0.00001028612,0.001546197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002994567,"threshold_uncertainty_score":0.01001781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03349971354482052,"score_gpt":0.2618149593882995,"score_spread":0.228315245843479,"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."}}