{"id":"W2322910032","doi":"10.6000/1927-5129.2012.08.02.28","title":"Exploration of Multiple Intelligence by Using Latent Class Model","year":2012,"lang":"en","type":"article","venue":"Journal of Basic & Applied Sciences","topic":"Emotional Intelligence and Performance","field":"Psychology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latent class model; Class (philosophy); Psychology; Set (abstract data type); Interpersonal communication; Competence (human resources); Goodness of fit; Latent variable; Social psychology; Mathematics; Computer science; Statistics; Artificial intelligence","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":[0.01080919,0.0011143,0.001240041,0.004785574,0.001217637,0.004566278,0.001370366,0.0007929697,0.003296949],"category_scores_gemma":[0.02196535,0.0004632958,0.002137986,0.003783189,0.001319226,0.003717498,0.002144954,0.002135162,0.0003990769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502874,"about_ca_system_score_gemma":0.001840165,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005387204,"about_ca_topic_score_gemma":0.003440415,"domain_scores_codex":[0.9886603,0.00836019,0.0002785259,0.001082583,0.00119204,0.0004263696],"domain_scores_gemma":[0.9803571,0.01617624,0.001313308,0.0009935586,0.0007873097,0.0003724454],"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":[0.00056617,0.001020926,0.271364,0.000518601,0.001395148,0.000546626,0.01176625,0.09948336,0.0008799728,0.3133303,0.005100869,0.2940278],"study_design_scores_gemma":[0.00007160123,0.000197116,0.02486863,0.000190086,0.0002152927,0.0002432914,0.002967255,0.7920262,0.0004499491,0.1737751,0.004891622,0.0001039436],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1970123,0.0006306702,0.7949783,0.001507608,0.00006708204,0.0002475104,0.0005060309,0.0003643886,0.004686066],"genre_scores_gemma":[0.8648239,0.0004473485,0.1322711,0.00006843055,0.00007070357,0.0003581682,0.0007464577,0.00005432416,0.00115948],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01080919,"threshold_uncertainty_score":0.05716521,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2101573018928446,"score_gpt":0.3825081659691011,"score_spread":0.1723508640762565,"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."}}