{"id":"W2914329342","doi":"10.1007/s13171-018-00159-8","title":"Parametric Inference Using Nomination Sampling with an Application to Mercury Contamination in Fish","year":2019,"lang":"en","type":"article","venue":"Sankhya A","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Parametric statistics; Sampling (signal processing); Nonparametric statistics; Inference; Estimator; Sampling distribution; Statistical inference; Statistics; Data mining; Mathematics; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0139914,0.0009258254,0.002312268,0.001436052,0.001636142,0.001442498,0.002743755,0.001667547,0.001742778],"category_scores_gemma":[0.05952008,0.0009856283,0.002260874,0.001932904,0.002746751,0.001668892,0.003303804,0.002854111,0.0002907107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007627688,"about_ca_system_score_gemma":0.001479567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005967435,"about_ca_topic_score_gemma":0.0050852,"domain_scores_codex":[0.9903755,0.007669437,0.0002710236,0.0008741603,0.0006145013,0.0001953881],"domain_scores_gemma":[0.9144598,0.07865243,0.001210989,0.002669177,0.002541485,0.0004660816],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005431897,0.0002941558,0.009659941,0.0005411723,0.0004873785,0.001063712,0.00161162,0.441998,0.006119527,0.1853504,0.002020979,0.3503099],"study_design_scores_gemma":[0.00002874869,0.00007552667,0.0008935559,0.00001802377,0.0000358006,0.0001444901,0.00006080057,0.9497629,0.001186104,0.04672581,0.001029743,0.00003861353],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00490002,0.00008697359,0.9947324,0.00004960566,0.00001600439,0.00001864475,0.000009948237,0.00006066357,0.0001256936],"genre_scores_gemma":[0.2440676,0.0003953882,0.7529699,0.00009969265,0.0001729304,0.0002968982,0.0001267812,0.0001238815,0.001746955],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0139914,"threshold_uncertainty_score":0.07399446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08629059038814736,"score_gpt":0.4057343504170169,"score_spread":0.3194437600288695,"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."}}