{"id":"W2087938803","doi":"10.1198/00401700152672555","title":"A Robust Criterion for Experimental Designs for Serially Correlated Observations","year":2001,"lang":"en","type":"article","venue":"Technometrics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Fractional factorial design; Mathematics; Simulated annealing; Algorithm; Variance (accounting); Factorial experiment; Design of experiments; Correlation; Statistics; Interval (graph theory); Mathematical optimization; Computer science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.003216381,0.0002551297,0.0004184981,0.001633605,0.0003941313,0.0003819684,0.001037269,0.0002496448,0.0002343505],"category_scores_gemma":[0.01014222,0.0002291993,0.0002571454,0.00704126,0.0001124643,0.000578039,0.0001579356,0.0001168816,0.000050983],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002423343,"about_ca_system_score_gemma":0.00008288745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001008146,"about_ca_topic_score_gemma":0.000001609067,"domain_scores_codex":[0.9970275,0.00008619179,0.0008745159,0.0007105304,0.0008220344,0.0004792348],"domain_scores_gemma":[0.9953382,0.002896565,0.0003092808,0.0006951826,0.0006041562,0.0001566097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009411403,0.001128658,0.002768955,0.00001612985,0.00005339212,0.000009471121,0.000546905,0.001414853,0.8887105,0.02476249,0.0262427,0.05340478],"study_design_scores_gemma":[0.008013995,0.005626437,0.004273654,0.00006032515,0.0001181048,0.00008926899,0.005519531,0.3138905,0.346124,0.05824064,0.2559737,0.002069816],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08513611,0.0005834277,0.9102013,0.000317759,0.0008465046,0.001881815,0.00009451379,0.0003001697,0.00063845],"genre_scores_gemma":[0.2688088,0.00001619942,0.7277157,0.0002489208,0.0001002857,0.0006841673,0.00003820007,0.00005046196,0.002337261],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5425865,"threshold_uncertainty_score":0.9981958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6859603614227414,"score_gpt":0.4947408768853512,"score_spread":0.1912194845373902,"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."}}