{"id":"W1983096469","doi":"10.1016/j.spl.2005.04.027","title":"Optimal allocation in balanced sampling","year":2005,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"","keywords":"Mathematics; Selection (genetic algorithm); Sampling (signal processing); Optimal allocation; Mathematical optimization; Statistics; Computer science; Artificial intelligence","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":[0.01021782,0.001371359,0.003501778,0.001703166,0.001486437,0.003148495,0.003440894,0.003043623,0.01159177],"category_scores_gemma":[0.04737245,0.001895764,0.001133332,0.002216691,0.003147782,0.006020665,0.003938042,0.002404272,0.001739064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002598899,"about_ca_system_score_gemma":0.002205158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002088407,"about_ca_topic_score_gemma":0.002278507,"domain_scores_codex":[0.9922719,0.005243098,0.0002260942,0.0008657753,0.000797749,0.0005954459],"domain_scores_gemma":[0.9815536,0.01505347,0.0005606322,0.001498808,0.0007771153,0.0005565273],"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":[0.000644599,0.0001593627,0.0009564554,0.0002186344,0.0001070446,0.00006430478,0.0002194826,0.1964164,0.0008470961,0.7276854,0.006858054,0.06582329],"study_design_scores_gemma":[0.0001131188,0.00003479709,0.0002059165,0.00002978548,0.00002096514,0.00002982215,0.00002343278,0.4348497,0.0002557103,0.5627073,0.001713427,0.00001598048],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01781773,0.0006699043,0.9740181,0.0009385548,0.00009911281,0.0001206834,0.0001582628,0.0002002565,0.005977399],"genre_scores_gemma":[0.4352455,0.001268608,0.5391108,0.000866079,0.0004653955,0.001412077,0.0008529046,0.0005028511,0.0202758],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01159177,"threshold_uncertainty_score":0.05403763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02596984586151083,"score_gpt":0.2886186013050123,"score_spread":0.2626487554435015,"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."}}