{"id":"W2770230962","doi":"10.1002/cjs.11496","title":"Checking validity of monotone domain mean estimators","year":2019,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Estimator; Monotonic function; Domain (mathematical analysis); Monotone polygon; Inference; Mathematics; Population; Applied mathematics; Mathematical optimization; Measure (data warehouse); Statistics; Computer science; Artificial intelligence; Mathematical analysis; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0546155,0.0005817464,0.001696137,0.003092175,0.001175909,0.002560275,0.003067815,0.001979357,0.002791009],"category_scores_gemma":[0.3103021,0.0007338428,0.001126563,0.001770649,0.00432818,0.00368841,0.004155281,0.002811201,0.0003899944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001457481,"about_ca_system_score_gemma":0.002756382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004016057,"about_ca_topic_score_gemma":0.002118013,"domain_scores_codex":[0.9719906,0.01872396,0.001069022,0.002800288,0.004782306,0.0006337016],"domain_scores_gemma":[0.6384281,0.3103216,0.014725,0.01668144,0.01769091,0.002152967],"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":[0.0006401323,0.000216215,0.1000345,0.0004908338,0.0004530177,0.0005911351,0.001218744,0.211301,0.004213267,0.5500008,0.004369155,0.1264712],"study_design_scores_gemma":[0.00006546103,0.0001410824,0.007087851,0.0001593068,0.00003286826,0.0002395105,0.0001994638,0.7217835,0.003075056,0.2652005,0.001961205,0.0000541939],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0900569,0.000415687,0.9054031,0.0005343282,0.0000314617,0.00008570385,0.0003393631,0.0001777392,0.002955649],"genre_scores_gemma":[0.7174053,0.0002377767,0.279714,0.0003033614,0.00008225154,0.0002622693,0.0008261359,0.0001526328,0.001016251],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0546155,"threshold_uncertainty_score":0.2888378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1065073843077188,"score_gpt":0.3567251940120499,"score_spread":0.2502178097043311,"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."}}