{"id":"W2578871007","doi":"","title":"Optimum Seating Arrangements and Tuscan Squares.","year":2016,"lang":"en","type":"article","venue":"Ars Combinatoria","topic":"Advanced Manufacturing and Logistics Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Mathematics; Least-squares function approximation; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004798837,0.00009317783,0.0000824204,0.0000342823,0.00005063447,0.00001804343,0.00005534189,0.00003667722,0.00002567011],"category_scores_gemma":[0.00003526826,0.00007498999,0.00001214544,0.00004118859,0.00002415872,0.000105832,0.00002378826,0.00004464956,0.00001338718],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004049327,"about_ca_system_score_gemma":0.00000280706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002805773,"about_ca_topic_score_gemma":5.458582e-7,"domain_scores_codex":[0.9995514,0.000007556293,0.00009572971,0.0001130129,0.00007242585,0.0001598922],"domain_scores_gemma":[0.999737,0.00004949154,0.00001727633,0.0001237722,0.00001844024,0.00005408191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000602682,0.0001573195,0.01751201,0.0004634085,0.0003080984,0.00004742177,0.001201203,0.2801112,0.01787141,0.457304,0.01316386,0.2117998],"study_design_scores_gemma":[0.007955707,0.0003243878,0.02070692,0.0005004117,0.00009363086,0.00001550184,0.0001991503,0.06946076,0.06585637,0.8178676,0.01507976,0.001939792],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8888566,0.0004364898,0.09206493,0.0001432901,0.002519884,0.0002619276,0.00001415136,0.0009682782,0.01473441],"genre_scores_gemma":[0.9969699,0.000101238,0.002691692,0.0000111751,0.000008604918,0.000009166448,0.000002956925,0.00002264279,0.0001826467],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3605636,"threshold_uncertainty_score":0.3058003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008008780175602014,"score_gpt":0.2084658774796507,"score_spread":0.2004570973040487,"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."}}