{"id":"W7100005018","doi":"","title":"LSAC RESEARCH REPORT SERIES � Modeling Nonignorable Missing Data Processes in Item Calibration","year":2006,"lang":"en","type":"article","venue":"","topic":"Bioactive Natural Diterpenoids Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Government (linguistics); Agency (philosophy); Corporation; Voting; Work (physics); Calibration; Missing data","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":[0.0647973,0.002853645,0.003340749,0.003224074,0.002554724,0.005099661,0.006336405,0.003678463,0.03345254],"category_scores_gemma":[0.1964486,0.002653661,0.006375262,0.007688069,0.002286921,0.00726363,0.004189109,0.008477548,0.005790949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004385286,"about_ca_system_score_gemma":0.0123052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09781719,"about_ca_topic_score_gemma":0.06895407,"domain_scores_codex":[0.9658895,0.02513816,0.001276454,0.004123479,0.002597901,0.0009745776],"domain_scores_gemma":[0.6730132,0.2674139,0.00851241,0.02494829,0.02378787,0.002324448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002072613,0.002595851,0.1037796,0.0008567219,0.004813379,0.0005612851,0.001437029,0.405565,0.0005294697,0.07580188,0.06160887,0.3403782],"study_design_scores_gemma":[0.0003554703,0.0005204796,0.01355553,0.0001857327,0.0005127666,0.0002001931,0.0003736067,0.9323493,0.0006203175,0.03763091,0.01356816,0.0001274646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.116038,0.00261575,0.8506318,0.006955112,0.00133271,0.001785747,0.008956358,0.003376393,0.008308056],"genre_scores_gemma":[0.3701836,0.001986877,0.5682913,0.001273668,0.001176421,0.003160998,0.0234782,0.001163106,0.02928594],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09781719,"threshold_uncertainty_score":0.342685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1056149832568717,"score_gpt":0.3978202577864625,"score_spread":0.2922052745295908,"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."}}