{"id":"W2241865608","doi":"","title":"The Variability of Pseudo R2s in Logistic Regression Models","year":2011,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Interpretability; Logistic regression; Contrast (vision); Econometrics; Variation (astronomy); Statistics; Regression analysis; Mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.2388745,0.003239804,0.003471261,0.007774913,0.002284867,0.007786321,0.005144875,0.003134612,0.001929877],"category_scores_gemma":[0.6133586,0.001687032,0.004276491,0.01066551,0.009028385,0.008164156,0.006085054,0.006522162,0.001927732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002173545,"about_ca_system_score_gemma":0.002466504,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003268467,"about_ca_topic_score_gemma":0.002684364,"domain_scores_codex":[0.6831167,0.2448792,0.01435955,0.02528268,0.03046119,0.001900628],"domain_scores_gemma":[0.2071082,0.7236741,0.02227344,0.03468167,0.01141175,0.0008508779],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002031292,0.000404858,0.2350199,0.004649961,0.01213122,0.003248911,0.008300424,0.1477053,0.004078429,0.149294,0.02346168,0.409674],"study_design_scores_gemma":[0.0002373042,0.001416795,0.09238122,0.002047741,0.002004189,0.00501062,0.002607492,0.3190254,0.008512162,0.5239438,0.04162895,0.001184313],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0828367,0.01503263,0.885349,0.003490405,0.001288793,0.0004685608,0.00133091,0.00238947,0.007813474],"genre_scores_gemma":[0.7747984,0.004342784,0.2096431,0.001697702,0.0008453122,0.001550038,0.002152709,0.002254854,0.002715182],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2388745,"threshold_uncertainty_score":0.9386033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.144050010518335,"score_gpt":0.4016378676439022,"score_spread":0.2575878571255672,"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."}}