{"id":"W7043449167","doi":"","title":"Statistical inference for sequential designs of
\\nrandomized clinical trials with binary responses","year":2023,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Coleoptera: Cerambycidae studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Sequential analysis; Binary data; Test statistic; Statistical hypothesis testing; Type I and type II errors; Sequential estimation; Markov chain; Binary number; Early stopping","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.007469313,0.0005535693,0.002151696,0.0005550717,0.001668558,0.0001757346,0.001440122,0.00091135,0.0001517785],"category_scores_gemma":[0.006288169,0.0003281389,0.0008646554,0.001918416,0.0008994769,0.0003531312,0.0004329388,0.001150698,0.00002947058],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702409,"about_ca_system_score_gemma":0.001433196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004497855,"about_ca_topic_score_gemma":0.01041552,"domain_scores_codex":[0.9865234,0.008763598,0.001024288,0.001323959,0.001459272,0.000905421],"domain_scores_gemma":[0.9677401,0.02842784,0.0009024445,0.0003125432,0.002160842,0.0004562886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"randomized_trial","study_design_gemma":"observational","study_design_scores_codex":[0.8642323,0.001298349,0.003117384,0.0006126696,0.003181651,0.004013954,0.001256794,0.00004801499,0.05084462,0.002027587,0.01068465,0.05868204],"study_design_scores_gemma":[0.1737893,0.06168519,0.350579,0.004270226,0.01238677,0.00005319009,0.1210174,0.0006610331,0.0438924,0.002994232,0.2192995,0.00937185],"study_design_candidate":"randomized_trial","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.948961,0.00004673055,0.0001136175,0.0001774626,0.01072929,0.00427192,0.001157303,0.0002668331,0.03427583],"genre_scores_gemma":[0.9290295,0.0005803964,0.0004623469,0.000006199992,0.009854125,0.00001191367,0.001840776,0.00002229745,0.0581924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.690443,"threshold_uncertainty_score":0.9999171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1892352157952659,"score_gpt":0.3937020906746595,"score_spread":0.2044668748793936,"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."}}