{"id":"W1514305813","doi":"","title":"Toward a model of language acquisition threshold","year":2006,"lang":"en","type":"article","venue":"international conference on Modelling and simulation","topic":"Fractal and DNA sequence analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Zipf's law; Computer science; Language model; Vocabulary; Natural language processing; Artificial intelligence; Graph; Rank (graph theory); Language acquisition; Clustering coefficient; Conjecture; Cluster analysis; Theoretical computer science; Linguistics; Mathematics; Statistics; Discrete mathematics","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":[0.0008963647,0.0002801709,0.0005104053,0.001288728,0.0005902478,0.001556685,0.001067879,0.001237709,0.005067422],"category_scores_gemma":[0.008511633,0.0002920214,0.0003952697,0.0005410853,0.001665299,0.003495125,0.001146347,0.00136,0.0006885267],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001239396,"about_ca_system_score_gemma":0.0005637506,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00205149,"about_ca_topic_score_gemma":0.001096222,"domain_scores_codex":[0.9996326,0.0001157115,0.00001036513,0.00007369322,0.00007257875,0.00009504208],"domain_scores_gemma":[0.9967009,0.001934833,0.0003809369,0.0002697156,0.0003656909,0.0003478491],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004685344,0.00005392577,0.002280471,0.00004682588,0.00001614292,0.0002084886,0.0005556674,0.1332832,0.005379931,0.8514695,0.001448221,0.005210858],"study_design_scores_gemma":[0.00001442425,0.00002070907,0.0007620115,0.00001379271,0.000005330328,0.00008668847,0.00007968284,0.5931904,0.0004011109,0.4044017,0.00101068,0.00001335248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5070384,0.0004873795,0.4341498,0.003932164,0.00005827994,0.00006438426,0.0002759496,0.0006387618,0.05335494],"genre_scores_gemma":[0.9795815,0.000182058,0.01569455,0.000143304,0.00003742247,0.00007728269,0.00006866013,0.00006843759,0.00414679],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005067422,"threshold_uncertainty_score":0.01695216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04732658598784076,"score_gpt":0.2964083551506053,"score_spread":0.2490817691627646,"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."}}