{"id":"W2138528407","doi":"10.1111/j.1541-0420.2011.01733.x","title":"Discussion of Adjustment Uncertainty and Propensity Scores","year":2012,"lang":"en","type":"letter","venue":"Biometrics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Citation; Library science; Computer science; Information retrieval","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03116032,0.0008822229,0.00166886,0.001518085,0.004607288,0.007136688,0.003555556,0.04800491,0.005704579],"category_scores_gemma":[0.1324385,0.0008459779,0.001744676,0.001336301,0.0120141,0.006622175,0.003286983,0.04048162,0.001894495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00639815,"about_ca_system_score_gemma":0.004487053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008334481,"about_ca_topic_score_gemma":0.01078967,"domain_scores_codex":[0.9685562,0.02037118,0.001621704,0.002242402,0.00590534,0.001303248],"domain_scores_gemma":[0.923179,0.06833892,0.001903467,0.002245461,0.003334695,0.0009984642],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00008383775,0.00002979366,0.000610126,0.00007834543,0.00005409091,0.00100035,0.0005154374,0.0006647464,0.0001159694,0.5415503,0.4377292,0.01756786],"study_design_scores_gemma":[0.0001236203,0.00002656285,0.001023483,0.0002633288,0.00005640677,0.0009513924,0.0003269562,0.003374076,0.0003275171,0.7343933,0.2590572,0.00007625578],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0004738806,0.001002,0.004513214,0.9820412,0.003993259,0.00001478619,0.00009393749,0.00001869788,0.007848967],"genre_scores_gemma":[0.03174403,0.001122024,0.006594773,0.9085964,0.03641645,0.0002016074,0.00004328579,0.00007246821,0.01520904],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9688397,"threshold_uncertainty_score":0.1647935,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1349039387862498,"score_gpt":0.3630317539314825,"score_spread":0.2281278151452326,"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."}}