{"id":"W7071671934","doi":"","title":"Some new computational methods in high-dimensional statistical learning in biostatistics","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Biostatistics; Statistical learning; Statistical analysis; Statistical model; Feature (linguistics)","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003283245,0.0008253844,0.001164466,0.001357237,0.0004430723,0.000203733,0.001200768,0.0008542239,0.00006741568],"category_scores_gemma":[0.002259197,0.0009192628,0.0001846477,0.001687972,0.00006144317,0.0008598138,0.0003841333,0.003083951,0.0002243445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006728915,"about_ca_system_score_gemma":0.000449647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001136367,"about_ca_topic_score_gemma":0.001033067,"domain_scores_codex":[0.992297,0.002181486,0.001517418,0.001814006,0.001143279,0.00104684],"domain_scores_gemma":[0.9950126,0.003074184,0.0005390973,0.0006186881,0.0002594066,0.0004960286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00004413082,0.00008500246,0.00001304276,0.00009084271,0.00003592178,0.0002890339,0.00002016528,0.003255894,0.0008340764,0.6049536,0.00002235803,0.3903559],"study_design_scores_gemma":[0.001309864,0.0001460037,0.00773043,0.0004191535,0.00004002397,0.00002462387,0.00002534706,0.03717068,0.001107919,0.9495302,0.001420269,0.001075431],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1436135,0.00160971,0.8225276,0.0005556712,0.01523325,0.003317988,0.002530275,0.002500709,0.008111217],"genre_scores_gemma":[0.02231987,0.00008752212,0.9707681,0.0003529464,0.00008889917,0.00006646169,0.001545159,0.0001775014,0.004593547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3892805,"threshold_uncertainty_score":0.9993258,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595892979567063,"score_gpt":0.3236612293358971,"score_spread":0.2977022995402265,"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."}}