{"id":"W1993481816","doi":"10.1002/cjs.11190","title":"The factor aliased effect number pattern and its application in experimental planning","year":2013,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; McMaster University","funders":"National Natural Science Foundation of China","keywords":"Fractional factorial design; Rank (graph theory); Ranking (information retrieval); Factorial experiment; Factor (programming language); Design of experiments; Computer science; Statistics; Paired comparison; Mathematics; Machine learning; Combinatorics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001073706,0.00009978627,0.0001835418,0.0001243793,0.0001449359,0.00031714,0.0003176382,0.00004132875,0.0003230914],"category_scores_gemma":[0.001055465,0.00006436049,0.00002489022,0.0001575444,0.00008463403,0.0002213082,0.00001602263,0.000160605,0.0000935523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001301841,"about_ca_system_score_gemma":0.0001690425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0020796,"about_ca_topic_score_gemma":0.001027428,"domain_scores_codex":[0.998474,0.0003059492,0.0004901476,0.0001274729,0.0003790784,0.000223342],"domain_scores_gemma":[0.9972944,0.001763965,0.0002533641,0.0001247076,0.0001611206,0.0004024524],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006242903,0.00003329935,0.5134249,0.00001106905,0.00004425645,0.0003077239,0.006942016,0.0001860969,0.04476497,0.00184394,0.02002789,0.4123514],"study_design_scores_gemma":[0.00295886,0.001359653,0.8060932,0.0001703645,0.00002894682,0.0005533012,0.01218833,0.04576333,0.1031988,0.01416523,0.01261558,0.0009043133],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9623229,0.0008106849,0.03577265,0.0001485103,0.0003083961,0.0002662224,0.00005467457,0.000001820164,0.0003141671],"genre_scores_gemma":[0.9929957,0.000003636459,0.006760514,0.0001063783,0.00004118722,0.00001022796,0.000001011042,0.00000960296,0.00007172389],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4114471,"threshold_uncertainty_score":0.3537624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08315733229787388,"score_gpt":0.4071847278924204,"score_spread":0.3240273955945466,"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."}}