{"id":"W1857882766","doi":"10.1111/biom.12380","title":"Optimum Study Design for Detecting Imprinting and Maternal Effects Based on Partial Likelihood","year":2015,"lang":"en","type":"article","venue":"Biometrics","topic":"Genetic Syndromes and Imprinting","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Imprinting (psychology); Statistics; Econometrics; Computer science; Mathematics; Biology; Genetics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008824722,0.0001564094,0.000147501,0.0002660365,0.00008960331,0.00008160075,0.0001278151,0.0000976329,8.602458e-7],"category_scores_gemma":[0.001103964,0.0001454644,0.00004989139,0.0003854969,0.00001815515,0.000002475079,0.0001185928,0.00005196198,0.000002946487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001623516,"about_ca_system_score_gemma":0.00005023293,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001344648,"about_ca_topic_score_gemma":5.957182e-7,"domain_scores_codex":[0.9988739,0.00007253364,0.0001908122,0.0003669665,0.0001709334,0.0003248101],"domain_scores_gemma":[0.999325,0.0001003949,0.00009458748,0.0002234874,0.0001003465,0.0001562156],"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":[0.0002201796,0.0005711753,0.2193412,0.0001626281,0.0001452786,0.00001049513,0.0001555685,0.001410497,0.5308626,0.00000412965,0.0002055787,0.2469106],"study_design_scores_gemma":[0.006821854,0.01232302,0.09724372,0.00004304591,0.0001048091,0.00003042041,0.000368659,0.04710907,0.8337055,0.00004220798,0.001570616,0.0006370412],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8405457,0.0001573899,0.1584046,0.00001279386,0.0002902247,0.0005393762,0.00000218103,0.00001474175,0.0000330353],"genre_scores_gemma":[0.9723721,0.000004032905,0.02726491,0.00005830594,0.0001939329,0.00005399263,0.000003958514,0.00002827828,0.00002047798],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3028429,"threshold_uncertainty_score":0.5931865,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03328857421749216,"score_gpt":0.2804025703448232,"score_spread":0.247113996127331,"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."}}