{"id":"W2023060931","doi":"10.1007/s00184-008-0173-8","title":"Minimax robust designs for field experiments","year":2008,"lang":"en","type":"article","venue":"Metrika","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Minimax; Mathematics; Estimator; Covariance matrix; Covariance; Mathematical optimization; Field (mathematics); Optimal design; Least-squares function approximation; Robust statistics; Spatial correlation; Algorithm; Statistics","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03657962,0.003682612,0.005258637,0.002041924,0.0008153769,0.00222914,0.003509535,0.003554074,0.00629438],"category_scores_gemma":[0.06897468,0.002805268,0.002620756,0.001351784,0.004004803,0.00255737,0.003305385,0.004763102,0.001209401],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002002813,"about_ca_system_score_gemma":0.003140971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008783143,"about_ca_topic_score_gemma":0.0006852656,"domain_scores_codex":[0.9719644,0.02273338,0.0006596721,0.002445285,0.001707415,0.0004898683],"domain_scores_gemma":[0.9419935,0.05042532,0.002929019,0.002599116,0.001665652,0.0003873262],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001912465,0.0003113899,0.000604681,0.001759743,0.0008129232,0.00009765154,0.000254935,0.4627683,0.005214498,0.3869208,0.002646095,0.1366966],"study_design_scores_gemma":[0.0004725892,0.0007612153,0.000503051,0.0001567993,0.0001259492,0.00004916754,0.0000242381,0.7483655,0.002676221,0.2427657,0.00403498,0.00006461484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008092526,0.0002374199,0.9982879,0.00007345666,0.0000236101,0.00009005318,0.00003481016,0.00008083008,0.0003626306],"genre_scores_gemma":[0.1129985,0.0007144328,0.8782745,0.0002530758,0.0001676645,0.004068854,0.000258537,0.0001905297,0.003073937],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9634204,"threshold_uncertainty_score":0.1934538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6510149411035311,"score_gpt":0.5186700726877603,"score_spread":0.1323448684157708,"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."}}