{"id":"W7100501300","doi":"","title":"Using Multiple Regression to Predict Minority Children&amp;apos;s Second Language Performance","year":2016,"lang":"en","type":"article","venue":"","topic":"Multilingual Education and Policy","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Residence; Comprehension; Immigration; Second language; First language; Regression analysis; Reflection (computer programming); Quality (philosophy)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.00111087,0.000520694,0.0002889674,0.0006989314,0.000319951,0.0007082064,0.0003041119,0.000238338,0.002214389],"category_scores_gemma":[0.005357289,0.0001423029,0.0005458923,0.0004484233,0.0002140618,0.0002844748,0.0003421174,0.0005629132,0.0004233194],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005775818,"about_ca_system_score_gemma":0.0008735246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09637386,"about_ca_topic_score_gemma":0.08717424,"domain_scores_codex":[0.9995981,0.0001779388,0.00001985371,0.00006709038,0.00007262547,0.00006439674],"domain_scores_gemma":[0.9981695,0.0008964816,0.0004336908,0.00007997541,0.0001856968,0.000234497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006967986,0.00005425535,0.9941121,0.000006003254,0.00007034668,0.00004231028,0.0002544009,0.0001828954,0.0002286382,0.00004001408,0.000113384,0.004825888],"study_design_scores_gemma":[0.00000867274,0.0001547561,0.994599,0.00001068284,0.00004735176,0.0000600759,0.0004807041,0.003943347,0.0002886221,0.00004569596,0.0003542084,0.000007013253],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990667,0.00007243742,0.0002381523,0.00004799126,0.000002981475,0.000005698687,0.0001141806,0.00001239392,0.0004396417],"genre_scores_gemma":[0.9987392,0.00005275321,0.0003846016,0.000005717464,0.000003192303,0.000009997752,0.0001619775,0.000003955997,0.0006386786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09637386,"threshold_uncertainty_score":0.1916258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1764924242545565,"score_gpt":0.4884638691332086,"score_spread":0.3119714448786521,"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."}}