{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001272448,0.00009200865,0.0001744596,0.0001400944,0.0001141569,0.00001832321,0.0003067718,0.00005961139,0.0001638395],"category_scores_gemma":[0.0000211057,0.00006975946,0.00006651554,0.0003114968,0.0002610729,0.0007917619,0.00002300132,0.0001618111,0.0000381076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003195476,"about_ca_system_score_gemma":0.00006828228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001564501,"about_ca_topic_score_gemma":0.000002348695,"domain_scores_codex":[0.9986669,0.00002999627,0.0005195398,0.0001095676,0.0004248035,0.0002492131],"domain_scores_gemma":[0.999083,0.0001092534,0.0005040775,0.00009690429,0.0001185733,0.00008820835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004656943,0.001747663,0.05394559,0.00006971385,0.0001514871,0.000003188885,0.03091567,0.4118014,0.2935611,0.1166962,0.007019146,0.08362316],"study_design_scores_gemma":[0.0007768561,0.00108428,0.004274795,0.000275938,0.000144994,0.0001853428,0.02231724,0.4146984,0.5223346,0.03003634,0.002971237,0.0008999843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8361227,0.0005759708,0.158011,0.0001312268,0.0005884986,0.00007541589,0.000003898471,0.000004570698,0.004486777],"genre_scores_gemma":[0.9955158,0.00007115729,0.004004485,0.0001327318,0.0001717082,0.000002299421,6.670198e-7,0.000005033562,0.00009617906],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2287735,"threshold_uncertainty_score":0.2844708,"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."}}