{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001085126,0.000113165,0.0001120042,0.0001299942,0.0001436083,0.0001602517,0.0004306817,0.0001901829,0.00003298907],"category_scores_gemma":[0.0008682942,0.00009997532,0.00001614414,0.0004667817,0.00009477173,0.00006096607,0.000460847,0.0002719202,0.000004499197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003706264,"about_ca_system_score_gemma":0.0004404808,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369245,"about_ca_topic_score_gemma":0.002920595,"domain_scores_codex":[0.9981519,0.0001354439,0.0002801543,0.0006285538,0.000403147,0.0004007745],"domain_scores_gemma":[0.998772,0.00003507622,0.00003646785,0.0006758154,0.0004289419,0.00005171236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002053432,0.0001557818,0.006558515,0.0001563129,0.00001529709,0.0001086211,0.00002429367,0.001523802,0.9819588,0.0001416789,0.007291948,0.001859571],"study_design_scores_gemma":[0.0004923504,0.0001877059,0.001059104,0.00008308123,0.000004868555,0.0001524637,0.0003533673,0.1188852,0.8575796,0.001644887,0.01913933,0.0004181085],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9589073,0.002681372,0.01414092,0.001689507,0.00007017054,0.0005740341,0.00004197969,0.00003934688,0.02185535],"genre_scores_gemma":[0.9890368,0.0001057628,0.003261791,0.00002886026,0.0002314795,0.00001828172,0.001483327,0.00002224642,0.005811449],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1243793,"threshold_uncertainty_score":0.4076875,"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."}}