{"id":"W2981493478","doi":"10.1002/bimj.201800095","title":"The one‐inflated positive Poisson mixture model for use in population size estimation","year":2019,"lang":"en","type":"article","venue":"Biometrical Journal","topic":"Census and Population Estimation","field":"Mathematics","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Estimator; Poisson distribution; Count data; Statistics; Boundary (topology); Inflation (cosmology); Mathematics; Econometrics; Population; Estimation; Mixture model; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.006831534,0.000893612,0.001518782,0.001926546,0.0009897311,0.0015774,0.004029024,0.002048363,0.006546109],"category_scores_gemma":[0.03743114,0.0008609175,0.002136418,0.003035599,0.00130041,0.002518504,0.003049028,0.004279227,0.0023341],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008055791,"about_ca_system_score_gemma":0.001579919,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003370014,"about_ca_topic_score_gemma":0.002647631,"domain_scores_codex":[0.9962406,0.002372486,0.0001640685,0.0004373662,0.0006474007,0.0001381129],"domain_scores_gemma":[0.9911872,0.006134798,0.0006576689,0.001290594,0.0005613406,0.000168379],"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":[0.0002216791,0.0001169951,0.006631576,0.0003674029,0.0002212473,0.0005068692,0.0004513352,0.2048829,0.003672414,0.5190371,0.01018399,0.2537064],"study_design_scores_gemma":[0.00002601077,0.00007702563,0.001661276,0.0001109419,0.00006318216,0.0005109404,0.00005668162,0.7298152,0.001134522,0.2513576,0.01509445,0.00009212345],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001473818,0.0001314065,0.9975436,0.0001077396,0.00005014418,0.00003603827,0.00009021469,0.0001771342,0.0003897563],"genre_scores_gemma":[0.1050104,0.0009737582,0.8855401,0.0003863721,0.0003374541,0.001239117,0.001591497,0.0005274003,0.004393968],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006831534,"threshold_uncertainty_score":0.03612906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07126103121012259,"score_gpt":0.3452760850243196,"score_spread":0.274015053814197,"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."}}