{"id":"W2970480640","doi":"10.1139/cjfr-2019-0170","title":"An application niche for finite mixture models in forest resource surveys","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; Canadian Forest Service","funders":"","keywords":"Statistics; Mathematics; Estimator; Variance (accounting); Population; Sampling (signal processing); Sample (material); Sample size determination; Mixture model; Econometrics; Computer science; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.01113203,0.000139968,0.0002810786,0.001017701,0.0001612222,0.0002799231,0.001920694,0.0001910473,0.000007978685],"category_scores_gemma":[0.0002860929,0.0001239176,0.0001052424,0.0009032141,0.00008824716,0.0007770966,0.00004591021,0.0007742444,0.00001005188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002358654,"about_ca_system_score_gemma":0.00191742,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004720825,"about_ca_topic_score_gemma":0.1665829,"domain_scores_codex":[0.9969702,0.0009674093,0.0004311096,0.0003604818,0.0004799648,0.0007908162],"domain_scores_gemma":[0.9968388,0.0006880037,0.0001398979,0.0007810591,0.0006980233,0.0008542181],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009094381,0.0001220042,0.200338,0.0001396405,0.00004164921,0.0001582878,0.003137083,0.05911548,0.0005439086,0.511304,0.005713981,0.219295],"study_design_scores_gemma":[0.001114388,0.0007682247,0.07101598,0.0001226565,0.00000431417,0.00006748443,0.00006287033,0.4804315,0.0001422154,0.4285015,0.01747744,0.0002914149],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08699442,0.0003489988,0.9094354,0.001250802,0.0001176434,0.0005396495,0.0000124231,0.000006349754,0.00129431],"genre_scores_gemma":[0.9199237,0.00001095515,0.07938636,0.0001297048,0.0001591564,0.00002516604,0.000008255501,0.00002252057,0.0003342411],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8329292,"threshold_uncertainty_score":0.8486248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05690737247889674,"score_gpt":0.3428419358459228,"score_spread":0.2859345633670261,"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."}}