{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007443394,0.0001364679,0.0001698397,0.00006903387,0.00005652874,0.00009280183,0.0004341409,0.00004742705,0.00001036792],"category_scores_gemma":[0.0001417997,0.0001386794,0.00002900257,0.0002195454,0.00006258552,0.0002905599,0.000093701,0.0001923321,0.00001776876],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001547514,"about_ca_system_score_gemma":0.00005610789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003013605,"about_ca_topic_score_gemma":0.00006369816,"domain_scores_codex":[0.9985415,0.000150466,0.0003268631,0.0004518411,0.0002026517,0.000326717],"domain_scores_gemma":[0.9991354,0.0001812112,0.00007130815,0.000485771,0.00005309643,0.00007319488],"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":[0.000008942139,0.00008122647,0.0006382311,0.00003500969,0.000006014819,0.000004955141,0.0009874926,0.001476904,0.004816459,0.7830955,0.0008242152,0.208025],"study_design_scores_gemma":[0.0007003828,0.00005543454,0.01421653,0.00003834255,0.000008439317,0.000009574233,0.000001420813,0.2144538,0.001177159,0.7663517,0.002481386,0.0005057621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0231535,0.00002115585,0.9699766,0.006244878,0.000129737,0.0002564175,0.00001274409,0.00007601036,0.0001289397],"genre_scores_gemma":[0.04454968,0.000003798014,0.9531981,0.002125543,0.00006653376,0.00002682866,0.000009038785,0.000007715415,0.0000127927],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2129769,"threshold_uncertainty_score":0.565518,"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."}}