{"id":"W2607428535","doi":"10.5539/ijsp.v6n3p132","title":"Self-Selecting Robust Logistic Regression Model","year":2017,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"African Union Commission; African Union","keywords":"Trimming; Outlier; Leverage (statistics); Logistic regression; Computer science; Robust regression; Bayesian probability; Statistics; Statistical model; Data mining; Binary data; Mathematics; Binary number","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001130957,0.0001263766,0.0002690655,0.00004395029,0.0002289194,0.0001603118,0.0003764594,0.00005635955,0.00001800663],"category_scores_gemma":[0.008895775,0.0000938136,0.00004721012,0.0000125149,0.000145484,0.0002134432,0.0001319464,0.000259528,4.971077e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007965688,"about_ca_system_score_gemma":0.0000939243,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000676401,"about_ca_topic_score_gemma":0.00001022475,"domain_scores_codex":[0.9986616,0.00007262405,0.0005460262,0.0001662961,0.000409436,0.0001439581],"domain_scores_gemma":[0.9967727,0.001054685,0.0008552928,0.0002121357,0.0009872459,0.0001179548],"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":[0.0001826239,0.0002318481,0.001298621,0.0001477624,0.0001187227,0.00007650142,0.0003140216,0.001146677,0.0001250287,0.9294333,0.0005002054,0.06642465],"study_design_scores_gemma":[0.0003859291,0.00007395778,0.0006299883,0.00007938011,0.00004118518,0.00005830619,0.00001113578,0.138272,0.00006673712,0.8601568,0.0001347761,0.00008985944],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01236618,0.00003879342,0.9858631,0.0003505453,0.000370753,0.00009383482,0.0001478185,0.00001078795,0.0007581978],"genre_scores_gemma":[0.28331,0.00008721781,0.7164165,0.00002176645,0.0001048656,0.000001552102,0.000001262436,0.000008836454,0.00004800294],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2709438,"threshold_uncertainty_score":0.9994527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2164328713275842,"score_gpt":0.4602158475967824,"score_spread":0.2437829762691982,"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."}}