{"id":"W134362049","doi":"","title":"Modeling Language Acquisition at Multiple Temporal Scales","year":2000,"lang":"en","type":"article","venue":"eScholarship (California Digital Library)","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Verb; Context (archaeology); Representation (politics); Linguistics; Computer science; Noun; Argument (complex analysis); Artificial intelligence; Natural language processing; Cognitive science; Psychology; Cognitive psychology; History","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001464794,0.0002888524,0.0002326073,0.0001268642,0.0002350032,0.001560333,0.001089676,0.0001438321,0.0005388973],"category_scores_gemma":[0.00004369099,0.0002759106,0.0001663868,0.0003526454,0.00003923812,0.006917603,0.0004818166,0.0002863208,0.004256211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005987387,"about_ca_system_score_gemma":0.00004405981,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001049892,"about_ca_topic_score_gemma":0.000004298783,"domain_scores_codex":[0.9978259,0.00006768022,0.0004432203,0.0006885737,0.0004412213,0.000533389],"domain_scores_gemma":[0.998758,0.00007080565,0.00005704415,0.0007958734,0.00002206006,0.0002962862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004171246,0.0008799742,0.3510588,0.00021744,0.0001391185,0.0006679439,0.001281919,0.04919079,0.002565304,0.007767453,0.002981296,0.5828329],"study_design_scores_gemma":[0.0007970296,0.00004882058,0.0009147236,0.00008439956,0.000005726629,0.00005072258,0.00003264231,0.9741006,0.002322912,0.007131426,0.01382769,0.0006833168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9114473,0.0003244655,0.07517716,0.0005329558,0.00009834375,0.000199249,0.0003183164,0.001074774,0.01082738],"genre_scores_gemma":[0.9775211,0.000009172866,0.01948878,0.0007678089,0.0001854029,0.00001532378,0.0003222155,0.00004771765,0.001642431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9249098,"threshold_uncertainty_score":0.9999693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01388182625061344,"score_gpt":0.2121777749374974,"score_spread":0.1982959486868839,"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."}}