{"id":"W2253505087","doi":"10.1002/sta4.136","title":"A second look at inference for bivariate Skellam distributions","year":2017,"lang":"en","type":"article","venue":"Stat","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Bivariate analysis; Inference; Computer science; Econometrics; Statistics; 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":[0.01123039,0.0008849347,0.001694061,0.002352157,0.0008562119,0.003229469,0.002235317,0.002454538,0.009244592],"category_scores_gemma":[0.05521708,0.0006851184,0.002233391,0.002660116,0.003084202,0.006334018,0.003452948,0.007072405,0.001380781],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009652596,"about_ca_system_score_gemma":0.001070257,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001251566,"about_ca_topic_score_gemma":0.001003209,"domain_scores_codex":[0.9951804,0.002882827,0.0001516045,0.0009824067,0.0006293032,0.0001734252],"domain_scores_gemma":[0.9701954,0.02177734,0.001400847,0.004120994,0.001951806,0.0005536022],"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":[0.00009775369,0.0000729337,0.002795058,0.000224463,0.0001974159,0.0003167756,0.0004001393,0.01577815,0.002031308,0.9145635,0.005271415,0.05825102],"study_design_scores_gemma":[0.00004274245,0.0001222119,0.002176059,0.0002338521,0.00008054504,0.0007719618,0.0001064538,0.1329461,0.001363436,0.8372358,0.02483147,0.00008931897],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006246281,0.001449059,0.9820775,0.003486934,0.0001848491,0.00001698672,0.0001536118,0.0001376857,0.00624699],"genre_scores_gemma":[0.335998,0.00569477,0.6357594,0.005132001,0.002681211,0.000186145,0.0006536257,0.0003950189,0.01349988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01123039,"threshold_uncertainty_score":0.05939269,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03388183594038812,"score_gpt":0.3351499821959251,"score_spread":0.3012681462555369,"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."}}