{"id":"W2512287401","doi":"10.1177/0008068320130104","title":"A Unified Approach to Factorial Designs with Randomization Restrictions","year":2013,"lang":"en","type":"article","venue":"Calcutta Statistical Association Bulletin","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Factorial experiment; Randomization; Factorial; Mathematics; Restricted randomization; Fractional factorial design; Isomorphism (crystallography); Linear subspace; Contrast (vision); Rank (graph theory); Plackett–Burman design; Mathematical optimization; Computer science; Arithmetic; Algorithm; Statistics; Combinatorics; Artificial intelligence; Pure mathematics; Clinical trial","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":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002870244,0.0002391959,0.0004895005,0.0002488059,0.0002916783,0.0007238619,0.0004244004,0.0001910965,0.003720274],"category_scores_gemma":[0.02285582,0.0001750479,0.00007645684,0.001143984,0.00006580781,0.0001792994,0.00008554036,0.0002509845,0.004218157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005188979,"about_ca_system_score_gemma":0.0001032484,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002365801,"about_ca_topic_score_gemma":0.000003007279,"domain_scores_codex":[0.9944329,0.001548928,0.0008394457,0.0006992929,0.001995623,0.0004838284],"domain_scores_gemma":[0.9901452,0.007746683,0.0003594869,0.0003889756,0.000933158,0.0004265093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008888562,0.0005767417,0.003923627,0.000005430135,0.000104038,0.000004701834,0.0008571562,0.00259518,0.0034642,0.08708046,0.8929904,0.007509276],"study_design_scores_gemma":[0.03446379,0.003350506,0.1621214,0.00007399081,0.0004396097,0.00003703848,0.004871062,0.06569102,0.005221863,0.09872101,0.6209722,0.004036461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002280968,0.000006593275,0.9683448,0.002204839,0.0003953855,0.001202579,0.0000905046,0.0001025769,0.02537173],"genre_scores_gemma":[0.2999159,0.000001992298,0.6866909,0.0007589152,0.0003177984,0.000477756,0.00007282222,0.00004113249,0.01172284],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2976349,"threshold_uncertainty_score":0.9971905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09177858634421168,"score_gpt":0.3715417699575644,"score_spread":0.2797631836133527,"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."}}