{"id":"W970665919","doi":"10.1007/978-3-319-13647-9_15","title":"How Predictive Is Tense for Language Profiency? A Cautionary Tale","year":2014,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Linguistics; Feature (linguistics); Natural language processing; Contrast (vision); Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004390974,0.0003072422,0.0003232002,0.0004163716,0.000187817,0.00014086,0.0005681971,0.0003375934,0.002447018],"category_scores_gemma":[0.0001097436,0.0002717099,0.0001325262,0.0001537994,0.0004561624,0.0001056888,0.0001379713,0.0005363083,0.00006997949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001024674,"about_ca_system_score_gemma":0.0001182544,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001622595,"about_ca_topic_score_gemma":0.000008194672,"domain_scores_codex":[0.9980109,0.00003960526,0.0002149551,0.0009547392,0.0003339701,0.000445852],"domain_scores_gemma":[0.9985414,0.000410679,0.0001911975,0.0006085745,0.0001482353,0.00009989],"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":[0.0001527191,0.0000753385,0.0000714108,0.0001327355,0.0000967419,0.0003886431,0.0523066,0.0008840789,0.0005392074,0.01688869,0.003454607,0.9250093],"study_design_scores_gemma":[0.01295385,0.009478735,0.006660612,0.003428373,0.0005317045,0.003673977,0.0007174997,0.3530167,0.007182585,0.1648142,0.4273071,0.01023466],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005753915,0.002490317,0.9840289,0.00124881,0.001518265,0.0006495584,0.00006117683,0.0001107841,0.009316835],"genre_scores_gemma":[0.7983791,0.000008921005,0.06232858,0.05864007,0.00508429,0.000235053,0.0002099621,0.0001761994,0.07493777],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9217003,"threshold_uncertainty_score":0.9999735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01375441549424083,"score_gpt":0.2805677527042854,"score_spread":0.2668133372100446,"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."}}