{"id":"W2163792624","doi":"10.1139/x09-019","title":"Systematic sampling of discrete and continuous populations: sample selection and the choice of estimator","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Census and Population Estimation","field":"Mathematics","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Selection (genetic algorithm); Sampling (signal processing); Sample size determination; Mathematics; Sampling design; Best linear unbiased prediction; Population; Inference; Population size; Sample (material); Econometrics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002152562,0.00006377521,0.0003149537,0.0003291669,0.0002049949,0.00004736765,0.00009552907,0.00004532574,0.000008447839],"category_scores_gemma":[0.007090139,0.00004290714,0.00004480132,0.000254448,0.0001494026,0.0001220852,0.000006603178,0.0001824961,9.755788e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000054538,"about_ca_system_score_gemma":0.0001880792,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.006773045,"about_ca_topic_score_gemma":0.03451882,"domain_scores_codex":[0.998685,0.00020567,0.0005919168,0.00006191829,0.0002897593,0.0001657426],"domain_scores_gemma":[0.9971564,0.001575473,0.0003855315,0.0001096946,0.0006065456,0.0001663574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000107122,0.00002695689,0.3837858,0.003946777,0.00008437649,0.000003465762,0.002324538,0.001092609,0.0001120126,0.6064457,0.0002959664,0.001774736],"study_design_scores_gemma":[0.00146928,0.0003372242,0.57902,0.003824756,0.0001273422,0.0001752968,0.0005070349,0.01784179,0.00005466882,0.3964808,0.00004931639,0.0001124417],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9895284,0.0007948005,0.008453989,0.0006894053,0.00003743462,0.0004080126,0.00001315753,0.000002140592,0.00007261462],"genre_scores_gemma":[0.9908832,0.00001374309,0.00902895,0.000005543256,0.00004083245,0.000002236763,0.000002142548,0.000006372257,0.00001701528],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2099649,"threshold_uncertainty_score":0.9998409,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1436628744441173,"score_gpt":0.4164620124043868,"score_spread":0.2727991379602694,"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."}}