{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01131251,0.001274522,0.002384233,0.001464989,0.0008080663,0.002326093,0.006367748,0.002541648,0.00644518],"category_scores_gemma":[0.02561755,0.001181607,0.002173076,0.002288452,0.002129073,0.004705734,0.002489432,0.004007816,0.001626729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001876022,"about_ca_system_score_gemma":0.001144969,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0113051,"about_ca_topic_score_gemma":0.01180347,"domain_scores_codex":[0.9956898,0.002159652,0.0002380481,0.001124877,0.0004313779,0.0003562223],"domain_scores_gemma":[0.9814557,0.01346992,0.002035556,0.001748986,0.0009668096,0.0003230762],"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":[0.0004255193,0.0002231467,0.0156683,0.0004288731,0.0002748144,0.0006754022,0.001068525,0.5578657,0.001736876,0.3725022,0.00432537,0.0448052],"study_design_scores_gemma":[0.00003064361,0.00003571354,0.001586368,0.00002988301,0.00002664502,0.00006579063,0.00006657834,0.9292278,0.0001450913,0.06703569,0.001715226,0.00003453196],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02374323,0.000355866,0.9729778,0.0004439179,0.00006593562,0.0001242212,0.001139528,0.0003163669,0.0008331217],"genre_scores_gemma":[0.6573569,0.001477852,0.3106372,0.0004924537,0.0003863111,0.001663086,0.006524736,0.0003818494,0.02107956],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01131251,"threshold_uncertainty_score":0.05982703,"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."}}