{"id":"W4307967006","doi":"10.1111/biom.13789","title":"Latent Multinomial Models for Extended Batch-Mark Data","year":2022,"lang":"en","type":"article","venue":"Biometrics","topic":"Census and Population Estimation","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Engineering and Physical Sciences Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Multinomial distribution; Data set; Computer science; Mark and recapture; Set (abstract data type); Latent variable; Synthetic data; Statistics; Transformation (genetics); Econometrics; Artificial intelligence; Mathematics; Population; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007659302,0.00009099971,0.0001404302,0.0005216078,0.000241972,0.00002780576,0.0003474719,0.00004037113,0.0001161977],"category_scores_gemma":[0.0007445462,0.00009211787,0.00005089798,0.001284721,0.00001084621,0.00013618,0.0003362338,0.00007564654,0.00000531841],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001074154,"about_ca_system_score_gemma":0.00003641699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002870306,"about_ca_topic_score_gemma":0.000003743805,"domain_scores_codex":[0.9989226,0.00003850162,0.000290475,0.0002470779,0.0003286378,0.0001726872],"domain_scores_gemma":[0.9986761,0.0004900135,0.0001582984,0.0005459365,0.00007906861,0.00005053944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003917485,0.002113489,0.003652735,0.0005130096,0.0001930179,0.00001086429,0.001151205,0.01293222,0.0009461679,0.274673,0.3968067,0.3066159],"study_design_scores_gemma":[0.0007235635,0.00007412283,0.001314344,0.000002834024,0.00004665032,0.000004905506,0.00003624195,0.885708,0.00003654559,0.05698774,0.05488508,0.0001799911],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1550939,0.0004566846,0.83122,0.001182998,0.002730018,0.002189615,0.004998429,0.0003632476,0.001765126],"genre_scores_gemma":[0.8707107,0.00001079039,0.1265979,0.00008299753,0.0001483718,0.00006880058,0.001335169,0.00003127903,0.001013998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8727757,"threshold_uncertainty_score":0.3756458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3146186061967376,"score_gpt":0.3923351724051062,"score_spread":0.07771656620836859,"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."}}