{"id":"W2132714854","doi":"10.1139/x00-138","title":"Variance and efficiency of the combined estimator in incomplete block designs of use in forest genetics: a numerical study","year":2001,"lang":"en","type":"article","venue":"Canadian Journal of Forest Research","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Statistics; Efficiency; Bias of an estimator; Mathematics; Efficient estimator; Minimum-variance unbiased estimator; Stein's unbiased risk estimate; Mean squared error; Variance (accounting); Monte Carlo method","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01040579,0.0001271742,0.0004967188,0.001422562,0.0001273796,0.0001386182,0.001424815,0.0000709599,0.00004813515],"category_scores_gemma":[0.008763894,0.00008647681,0.00007801814,0.00295619,0.0007182786,0.0002248834,0.0001503018,0.0005511886,0.000002625153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002065298,"about_ca_system_score_gemma":0.001703761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01662857,"about_ca_topic_score_gemma":0.09500267,"domain_scores_codex":[0.9944386,0.001988993,0.001194357,0.000267824,0.001579216,0.0005309897],"domain_scores_gemma":[0.9949754,0.002973821,0.0003306953,0.0005069879,0.0007206348,0.000492494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001692319,0.0002007353,0.9898662,0.000004317642,0.000007838034,0.0002472057,0.001138783,0.006212686,0.0009865661,0.0005072647,0.00007378021,0.0005853864],"study_design_scores_gemma":[0.001229188,0.001417276,0.981342,0.00009388149,0.000003739598,0.00008940232,0.001399711,0.009469681,0.0003352122,0.00447341,0.00006745484,0.00007908289],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973666,0.0003285382,0.001213897,0.0002444494,0.00008900007,0.0005503702,0.000006657138,7.362931e-7,0.0001997438],"genre_scores_gemma":[0.992627,0.000007818914,0.007264683,0.00001492527,0.00001507638,0.00000631391,8.34902e-8,0.00001205579,0.00005204298],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07837409,"threshold_uncertainty_score":0.9995857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.314595879346545,"score_gpt":0.474137970425894,"score_spread":0.1595420910793489,"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."}}