{"id":"W2044746424","doi":"10.1075/lia.1.2.05car","title":"Explaining how learners extract ‘formulae’ from L2 input","year":2010,"lang":"en","type":"article","venue":"Language Interaction and Acquisition","topic":"Natural Language Processing Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Canada Research Chairs","keywords":"Segmentation; Syllable; Computer science; Linguistics; Speech segmentation; Natural language processing; Tracking (education); Speech recognition; Artificial intelligence; Mathematics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001455384,0.0001365362,0.0001138655,0.0001367829,0.0001250813,0.0005596737,0.0002646979,0.0001131921,0.0001522563],"category_scores_gemma":[0.00007334678,0.0001208373,0.00004195131,0.0001386203,0.00002790597,0.001905268,0.0001129654,0.0004539832,0.0000227457],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002499585,"about_ca_system_score_gemma":0.00001183484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001408995,"about_ca_topic_score_gemma":0.00003628351,"domain_scores_codex":[0.9991817,0.00003633757,0.0001270595,0.000320776,0.0001663636,0.0001677396],"domain_scores_gemma":[0.9993712,0.00009191195,0.0001255638,0.0002879558,0.00004901929,0.00007431442],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001961242,0.00003102735,0.0001717238,0.00001043706,0.00001138741,0.00006517687,0.00579472,8.306344e-7,0.6583346,0.00516911,0.0004588364,0.3299325],"study_design_scores_gemma":[0.001171858,0.0002266446,0.002009529,0.0003364995,0.00003361739,0.0005146731,0.009944195,0.04807912,0.9130402,0.01509717,0.008393361,0.001153146],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7421712,0.0004957192,0.2530667,0.001716155,0.0007833011,0.00009858613,0.000005083317,0.0007775048,0.0008857622],"genre_scores_gemma":[0.8745641,0.000009361245,0.1241748,0.0007746398,0.0002500639,0.000009469161,0.00003426379,0.00000995545,0.000173389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3287794,"threshold_uncertainty_score":0.5396946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00865952947203548,"score_gpt":0.2754773890086907,"score_spread":0.2668178595366552,"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."}}