{"id":"W2950867586","doi":"10.48550/arxiv.1605.08473","title":"Robust designs for experiments with blocks","year":2016,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Minimax; Estimator; Covariance matrix; Algorithm; Simulated annealing; Covariance; Construct (python library); Mathematical optimization; Mathematics; Design matrix; Matrix (chemical analysis); Computer science; Least-squares function approximation; Design of experiments; Generalized least squares; Minimax estimator; Applied mathematics; Linear model; Statistics; Minimum-variance unbiased estimator","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00180736,0.0005651509,0.0007512579,0.0005124338,0.0002806406,0.0002745507,0.002480476,0.0004349926,0.0007226186],"category_scores_gemma":[0.0004663762,0.0004194623,0.0004061779,0.0006271692,0.0003885236,0.0004166899,0.00126283,0.0003389424,0.0002159903],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003701819,"about_ca_system_score_gemma":0.0003206078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003019765,"about_ca_topic_score_gemma":0.00000742261,"domain_scores_codex":[0.9956665,0.000511993,0.0004991253,0.002219446,0.0004769789,0.0006259817],"domain_scores_gemma":[0.9949781,0.001558582,0.0006164861,0.001901476,0.0005746332,0.0003707275],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.01173941,0.002285539,0.02232849,0.0001760865,0.002028891,0.001600452,0.003196031,0.6414355,0.04842363,0.2044411,0.04695733,0.01538761],"study_design_scores_gemma":[0.01200673,0.002873851,0.001021061,0.00104315,0.0006984721,0.00004461743,0.00521869,0.2189881,0.1467268,0.5893358,0.01643307,0.005609605],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08299574,0.0001180633,0.9014995,0.00006658473,0.0006630169,0.001143434,0.00009776812,0.0001361172,0.01327982],"genre_scores_gemma":[0.9065931,0.00002205836,0.07017016,0.0001018879,0.0001449297,0.00001678082,0.000009474876,0.00006456069,0.02287702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8313293,"threshold_uncertainty_score":0.9998257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5308108886623619,"score_gpt":0.340477567155548,"score_spread":0.1903333215068139,"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."}}