{"id":"W1995348618","doi":"10.1080/00220970109599488","title":"Minimizing the Influence of Item Parameter Estimation Errors in Test Development: A Comparison of Three Selection Procedures","year":2001,"lang":"en","type":"article","venue":"The Journal of Experimental Education","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Item response theory; Selection (genetic algorithm); Statistics; Computerized adaptive testing; Function (biology); Test (biology); Range (aeronautics); Estimation; Mathematics; Equating; Estimation theory; Computer science; Artificial intelligence; Psychometrics; Rasch model; Engineering","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":[0.1649755,0.001984952,0.001649823,0.003399379,0.0009233931,0.003196165,0.002126873,0.002167293,0.0009093232],"category_scores_gemma":[0.4805595,0.001264832,0.002620338,0.003735655,0.00192576,0.003600333,0.003186702,0.002337766,0.0003888695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001446426,"about_ca_system_score_gemma":0.002640103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001801226,"about_ca_topic_score_gemma":0.001995937,"domain_scores_codex":[0.782505,0.1913942,0.008598996,0.003653907,0.01303872,0.0008091899],"domain_scores_gemma":[0.2580056,0.6905779,0.01710973,0.01909249,0.01402189,0.00119252],"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":[0.02385446,0.003100528,0.1244754,0.001755891,0.005693099,0.0002159473,0.004870731,0.04662762,0.008256217,0.006547438,0.001983943,0.7726188],"study_design_scores_gemma":[0.007669538,0.029177,0.3463097,0.002436862,0.008904736,0.00112541,0.002902927,0.5348985,0.04164823,0.01752995,0.006024001,0.001373114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6668624,0.003791996,0.3224353,0.0008984664,0.0001853531,0.001649149,0.0001990518,0.001004034,0.002974104],"genre_scores_gemma":[0.7313516,0.001132023,0.264311,0.0003691828,0.00007019588,0.001490747,0.0003043689,0.0004704168,0.0005004596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1649755,"threshold_uncertainty_score":0.8724842,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2827474909716795,"score_gpt":0.4893092776194928,"score_spread":0.2065617866478133,"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."}}