{"id":"W4251323366","doi":"10.3138/cmlr.63.1.83","title":"From <i>Faible</i> to Strong: How Does Their Vocabulary Grow?","year":2006,"lang":"en","type":"article","venue":"Canadian Modern Language Review/ La Revue canadienne des langues vivantes","topic":"Second Language Acquisition and Learning","field":"Psychology","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vocabulary; Active listening; Linguistics; Variety (cybernetics); Index (typography); Feature (linguistics); Character (mathematics); Word (group theory); Cognate; Psychology; Word lists by frequency; Narrative; Vocabulary development; Computer science; Artificial intelligence; Communication; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008529985,0.0001620463,0.0002636864,0.001053853,0.0006672493,0.00230619,0.0005127987,0.00050967,0.002407731],"category_scores_gemma":[0.004969092,0.0001244817,0.0001712419,0.00050118,0.001095558,0.001477619,0.0007564764,0.0007097847,0.0006953501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006929233,"about_ca_system_score_gemma":0.00074693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02335857,"about_ca_topic_score_gemma":0.02566925,"domain_scores_codex":[0.9996353,0.00006308241,0.00002507398,0.00006282137,0.000118652,0.00009499531],"domain_scores_gemma":[0.9973351,0.0008790633,0.0007258601,0.00007783016,0.0004754587,0.0005066976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002432216,0.0002234131,0.8548824,0.0001051461,0.00002467627,0.001947322,0.06854082,0.00007712228,0.008966338,0.0006713117,0.001307954,0.06301026],"study_design_scores_gemma":[0.00001077406,0.0003131322,0.9172307,0.00008003064,0.00002484351,0.001389144,0.07237297,0.0001835382,0.001348965,0.0007168293,0.006300622,0.00002846367],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981012,0.0001430033,0.00002750183,0.00008740648,0.000002857932,0.000003649185,0.00004103756,0.000002780261,0.001590536],"genre_scores_gemma":[0.9987891,0.0001522771,0.00006384073,0.00003717701,0.000003019857,0.000006178401,0.00005746359,0.00000311666,0.0008878919],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02335857,"threshold_uncertainty_score":0.04644519,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077754589449968,"score_gpt":0.2394836871162476,"score_spread":0.2287061412217479,"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."}}