{"id":"W2564073320","doi":"10.82308/48238","title":"Targeting count and noncount nouns in English through textual enhancement and elaboration tasks: effects on L2 development and text comprehension","year":2012,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Grammaticality; Elaboration; Comprehension; Psychology; Task (project management); Noun; Intervention (counseling); Cognitive psychology; Linguistics; Computer science; Natural language processing; Grammar","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001161566,0.0003650424,0.0003657685,0.0001732718,0.0005909738,0.00007674338,0.00009091352,0.0002518419,0.0008674992],"category_scores_gemma":[0.000271073,0.0003583232,0.00002635103,0.0002285605,0.00006942532,0.0006926829,0.0001192757,0.0006319183,0.0001060467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002550319,"about_ca_system_score_gemma":0.00001255286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009921085,"about_ca_topic_score_gemma":0.00004801154,"domain_scores_codex":[0.9974695,0.0004695675,0.0005110585,0.0006261849,0.0003222611,0.0006014601],"domain_scores_gemma":[0.9987137,0.0005207286,0.0002048618,0.0002368371,0.0001167987,0.0002071309],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008263512,0.001266732,0.00594926,0.000520866,0.0003186057,0.000210584,0.01335562,0.00003453414,0.1192985,0.1069206,0.0001345022,0.7511639],"study_design_scores_gemma":[0.01205547,0.00130517,0.147513,0.001184001,0.0001557745,0.0001501722,0.02712262,0.0001429192,0.07996842,0.000775674,0.7269111,0.002715689],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9849654,0.004237331,0.000006979792,0.00002488762,0.0005953484,0.0005164202,0.00002955054,0.00009359837,0.009530479],"genre_scores_gemma":[0.9949061,0.0001019164,0.001241004,0.003251538,0.0001227817,0.00009269061,0.00007702797,0.00004842256,0.0001584819],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7484482,"threshold_uncertainty_score":0.9998869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141347193796847,"score_gpt":0.2637322956961753,"score_spread":0.2495975763164907,"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."}}