{"id":"W3169510102","doi":"10.5539/elt.v14n7p8","title":"Effectiveness of Corpus in Distinguishing Two Near-Synonymous Verbs: Damage and Destroy","year":2021,"lang":"en","type":"article","venue":"English Language Teaching","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Collocation (remote sensing); Sketch; Meaning (existential); Corpus linguistics; Vocabulary; Linguistics; Psychology; British National Corpus; Computer science; Natural language processing; Artificial intelligence; Philosophy","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001574674,0.0001712752,0.0003302713,0.00009998746,0.0001196033,0.000100841,0.0001391745,0.00009467188,0.001567535],"category_scores_gemma":[0.002562898,0.0001841656,0.00006314978,0.0001844769,0.00006851363,0.0001445352,0.0001048191,0.0007803257,0.000009028949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004763169,"about_ca_system_score_gemma":0.00003194536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001332871,"about_ca_topic_score_gemma":0.00008254419,"domain_scores_codex":[0.9973897,0.001465621,0.0002817932,0.0004020828,0.0001412863,0.0003195468],"domain_scores_gemma":[0.9982843,0.001115216,0.0001176679,0.0003594721,0.00004254372,0.00008080801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002420754,0.0004564451,0.07279801,0.0004719771,0.0001525329,0.007840767,0.7844037,0.000338974,0.03690441,0.01114378,0.00007103448,0.08517624],"study_design_scores_gemma":[0.01352071,0.0003373288,0.3739393,0.002174204,0.000156404,0.0005977662,0.5909444,0.0009830146,0.01292294,0.0002622126,0.002665675,0.001496064],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9648343,0.006690855,0.0002772012,0.000009554418,0.0005051469,0.0001217476,0.00001030259,0.0001180706,0.0274328],"genre_scores_gemma":[0.9980959,0.000001540833,0.001076827,0.0002604541,0.0002090258,0.00001369862,0.00006112787,0.00003774546,0.0002437253],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3011413,"threshold_uncertainty_score":0.9993452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007786446757205121,"score_gpt":0.3028608271093475,"score_spread":0.2950743803521424,"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."}}