{"id":"W57323835","doi":"10.1007/978-1-4613-0049-6_6","title":"Designs in the Presence of Trends","year":2002,"lang":"en","type":"book-chapter","venue":"Lecture notes in statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Mathematics; TRACE (psycholinguistics); Block (permutation group theory); Class (philosophy); Block size; Optimal design; Block design; Degree (music); Matrix (chemical analysis); Binary number; Term (time); Combinatorics; Mathematical optimization; Computer science; Arithmetic; Statistics; 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":[0.06514648,0.001537584,0.002763296,0.001248909,0.0006415292,0.002574876,0.002711415,0.002927213,0.01061535],"category_scores_gemma":[0.114963,0.001835502,0.001688533,0.001420829,0.00214272,0.003637047,0.001539463,0.003628785,0.002144093],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001257882,"about_ca_system_score_gemma":0.001995657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003580121,"about_ca_topic_score_gemma":0.0008548126,"domain_scores_codex":[0.9415805,0.04716954,0.001595518,0.0041034,0.005045731,0.000505134],"domain_scores_gemma":[0.8812483,0.1040034,0.003691461,0.008258165,0.002420822,0.0003778153],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001560651,0.0002973727,0.002531728,0.002036331,0.00104538,0.0001899595,0.0005717887,0.0301529,0.003342107,0.5383824,0.009857789,0.4100316],"study_design_scores_gemma":[0.0007424388,0.001377828,0.00198144,0.0004217576,0.0005471733,0.0002422625,0.0001186487,0.08764253,0.004834587,0.8711168,0.03088788,0.00008672563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003364405,0.001507371,0.9883577,0.0004545966,0.0002833135,0.0003161269,0.0001678108,0.0002489394,0.00529981],"genre_scores_gemma":[0.07509367,0.001919858,0.909879,0.0008607871,0.0002968167,0.003571935,0.0003224591,0.0001766046,0.007878732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.06514648,"threshold_uncertainty_score":0.3445316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2325307607797628,"score_gpt":0.4434055247947282,"score_spread":0.2108747640149654,"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."}}