{"id":"W1797404590","doi":"","title":"A Corpus Investigation: The Similarities and Differences of cute, pretty and beautiful","year":2015,"lang":"en","type":"article","venue":"3L: Language, Linguistics, Literature®","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Intuition; Collocation (remote sensing); Corpus linguistics; Linguistics; Computer science; Natural language processing; Phraseology; Prosody; Artificial intelligence; Psychology; Cognitive science; Philosophy","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003610822,0.0002911099,0.0003651889,0.006214112,0.002550181,0.001788169,0.0004273217,0.0003961764,0.002114043],"category_scores_gemma":[0.01727934,0.0002487762,0.0002615961,0.006325186,0.002513961,0.001767271,0.001864468,0.0006717399,0.0002318659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009024583,"about_ca_system_score_gemma":0.00104089,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003358254,"about_ca_topic_score_gemma":0.007298493,"domain_scores_codex":[0.9960454,0.00195839,0.0005641059,0.0006136881,0.0007122451,0.0001061671],"domain_scores_gemma":[0.9768858,0.01636729,0.001488334,0.001832208,0.003175374,0.0002511269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.001321187,0.0007066325,0.146716,0.002548363,0.0002373669,0.002465585,0.4315818,0.0003951541,0.1019305,0.02980269,0.007254405,0.2750404],"study_design_scores_gemma":[0.0001351947,0.0009917127,0.5207018,0.0008325857,0.0003681676,0.007637745,0.3012018,0.003321798,0.03867322,0.008141521,0.1177201,0.0002744692],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9731845,0.001197145,0.00979572,0.0002166426,0.00009614648,0.0004327582,0.0007456779,0.00004311335,0.01428838],"genre_scores_gemma":[0.9777083,0.0007409554,0.01777262,0.00009883702,0.00003430338,0.0007471108,0.0009478332,0.00006248411,0.001887536],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006214112,"threshold_uncertainty_score":0.01909608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02763101673302199,"score_gpt":0.2951634676658851,"score_spread":0.2675324509328631,"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."}}