{"id":"W2157093406","doi":"10.1002/cjs.11255","title":"Flexible risk‐adjusted surveillance procedures for autocorrelated binary series","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Advanced Statistical Process Monitoring","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Actua; University of Waterloo; University of Windsor; University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Statistics; Logistic regression; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002066114,0.0001864219,0.0004444639,0.0004289673,0.0002897286,0.0002558545,0.0006491538,0.00009633033,0.00006892056],"category_scores_gemma":[0.06255778,0.0001529023,0.00005896061,0.0006647774,0.0002655042,0.0005019901,0.00002260069,0.0003387546,0.00003735427],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002542423,"about_ca_system_score_gemma":0.004403474,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005212576,"about_ca_topic_score_gemma":0.01079597,"domain_scores_codex":[0.9972495,0.0001417827,0.001053654,0.0002523783,0.0008146351,0.000488096],"domain_scores_gemma":[0.991689,0.002055306,0.0008946443,0.0002472076,0.003714612,0.001399243],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001167445,0.00008240966,0.150032,0.0001830543,0.0002089975,0.001645931,0.004470689,0.06747927,0.00009133216,0.06382807,0.5980729,0.1127379],"study_design_scores_gemma":[0.002466638,0.001807378,0.04839782,0.0001537603,0.00008603802,0.0005439215,0.004153952,0.01181365,0.0002495413,0.8161117,0.1134878,0.0007277936],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008341604,0.0008321971,0.984892,0.0004365442,0.002269557,0.0003007529,0.002404498,0.00002145925,0.0005013476],"genre_scores_gemma":[0.7436036,0.00002642477,0.253754,0.000067805,0.0003525163,0.00001032978,0.00001643371,0.00004008915,0.002128742],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7522837,"threshold_uncertainty_score":0.9453387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1448423953760603,"score_gpt":0.3746836812500396,"score_spread":0.2298412858739793,"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."}}